# Conditional Expectation under Change of Measure

## Theorem

Let $\mathbb{P}$ and $\mathbb{Q}$ be measures on $(\Omega, {\mathcal{F}})$ with $\mathbb{Q} \ll \mathbb{P}$, ${\mathcal{G}} \subset {\mathcal{F}}$ a sub $\sigma$-algebra and $X$ an integrable random variable ($\mathbb{P}\lvert{X}\rvert < \infty$) then

$\displaystyle \mathbb{P}(X\vert {\mathcal{G}}) = \frac {\mathbb{Q}\bigg(X\frac{\mathrm{d}\mathbb{P}}{\mathrm{d}\mathbb{Q}}\bigg\vert {\mathcal{G}}\bigg)} {\mathbb{Q}\bigg(\frac{\mathrm{d}\mathbb{P}}{\mathrm{d}\mathbb{Q}}\bigg\vert {\mathcal{G}}\bigg)}$

## Proof

\displaystyle \begin{aligned} \mathbb{Q}\bigg(\mathbb{I}_A \mathbb{Q}\bigg(X \frac{\mathrm{d}\mathbb{P}}{\mathrm{d}\mathbb{P}}\bigg\vert {\mathcal{G}}\bigg)\bigg) &= \mathbb{Q}\bigg(\mathbb{I}_A X \frac{\mathrm{d}\mathbb{P}}{\mathrm{d}\mathbb{P}}\bigg) \\ &= \mathbb{P}\big(\mathbb{I}_A X \big) \\ &= \mathbb{P}\big(\mathbb{I}_A \mathbb{P}(X \vert {\mathcal{G}})\big) \\ &= \mathbb{Q}\bigg(\mathbb{I}_A \frac{\mathrm{d}\mathbb{P}}{\mathrm{d}\mathbb{Q}}\mathbb{P}(X \vert {\mathcal{G}})\bigg) \\ &= \mathbb{Q}\bigg(\mathbb{I}_A \mathbb{Q}\bigg(\frac{\mathrm{d}\mathbb{P}}{\mathrm{d}\mathbb{Q}}\bigg\vert {\mathcal{G}}\bigg)\mathbb{P}(X \vert {\mathcal{G}})\bigg) \\ \end{aligned}

Thus

$\displaystyle \mathbb{Q}\bigg(\mathbb{I}_A\mathbb{P}(X\vert {\mathcal{G}}){\mathbb{Q}\bigg(\frac{\mathrm{d}\mathbb{P}}{\mathrm{d}\mathbb{Q}}\bigg\vert {\mathcal{G}}\bigg)}\bigg) = \mathbb{Q}\bigg(\mathbb{I}_A \mathbb{Q}\bigg(X\frac{\mathrm{d}\mathbb{P}}{\mathrm{d}\mathbb{Q}}\bigg\vert {\mathcal{G}}\bigg)\bigg)\quad \mathrm{for\,all}\, A \in {\mathcal{G}}$

Hence

$\displaystyle \mathbb{P}(X\vert {\mathcal{G}}){\mathbb{Q}\bigg(\frac{\mathrm{d}\mathbb{P}}{\mathrm{d}\mathbb{Q}}\bigg\vert {\mathcal{G}}\bigg)} = \mathbb{Q}\bigg(X\frac{\mathrm{d}\mathbb{P}}{\mathrm{d}\mathbb{Q}}\bigg\vert {\mathcal{G}}\bigg)\quad {\mathbb{Q}-\mathrm{a.s.}}$

We note that

$\displaystyle A = \bigg\{\omega \,\bigg\vert\, \mathbb{Q}\bigg(\frac{\mathrm{d}\mathbb{P}}{\mathrm{d}\mathbb{Q}}\bigg)\bigg\}$

is ${\mathcal{G}}$-measurable (it is the result of a projection) and that

$\displaystyle 0 = \mathbb{Q}\bigg(\mathbb{I}_A\mathbb{Q}\bigg( \frac{\mathrm{d}\mathbb{P}}{\mathrm{d}\mathbb{Q}} \bigg\vert {\mathcal{G}}\bigg)\bigg) = \mathbb{Q}\bigg(\mathbb{I}_A \frac{\mathrm{d}\mathbb{P}}{\mathrm{d}\mathbb{Q}} \bigg) = \mathbb{P}(\mathbb{I}_A)$

Hence

$\displaystyle \mathbb{P}(X\vert {\mathcal{G}}) = \frac {\mathbb{Q}\bigg(X\frac{\mathrm{d}\mathbb{P}}{\mathrm{d}\mathbb{Q}}\bigg\vert {\mathcal{G}}\bigg)} {\mathbb{Q}\bigg(\frac{\mathrm{d}\mathbb{P}}{\mathrm{d}\mathbb{Q}}\bigg\vert {\mathcal{G}}\bigg)}\quad {\mathbb{P}-\mathrm{a.s.}}$

as required.

# Introduction

If you look at the wikipedia article on Hidden Markov Models (HMMs) then you might be forgiven for concluding that these deal only with discrete time and finite state spaces. In fact, HMMs are much more general. Furthermore, a better understanding of such models can be helped by putting them into context. Before actually specifying what an HMM is, let us review something of Markov processes. A subsequent blog post will cover HMMs themselves.

# Markov Process and Chains

Recall that a transition kernel is a mapping $\mu : X \times {\cal{Y}} \rightarrow \overline{\mathbb{R}}_{+}$ where $(X, {\cal{X}})$ and $(Y, {\cal{Y}})$ are two measurable spaces such that $\mu(s, \cdot)$ is a probability measure on ${\cal{Y}}$ for all $s \in X$ and such that $\mu(\cdot, A)$ is a measurable function on $X$ for all $A \in {\cal{Y}}$.

For example, we could have $X = Y = \{a, b\}$ and ${\cal{X}} = {\cal{Y}} = \{\emptyset, \{a\}, \{b\}, \{a,b\}\}$ and $\mu(a,\{a\}) = 0.4, \mu(a,\{b\}) = 0.6, \mu(b,\{a\}) = 0.6, \mu(b,\{b\}) = 0.4$. Hopefully this should remind you of the transition matrix of a Markov chain.

Recall further that a family of such transitions $\{\mu_t\}_{t \in T}$ where $T$ is some index set satisfying

$\displaystyle \mu_{t+s}(x, A) = \int_{Y} \mu_s(x, {\mathrm{d}y})\mu_t(y, A)$

gives rise to a Markov process (under some mild conditions — see Rogers and Williams (2000) and Kallenberg (2002) for much more detail), that is, a process in which what happens next only depends on where the process is now and not how it got there.

Let us carry on with our example and take $T = \mathbb{N}$. With a slight abuse of notation and since $Y$ is finite we can re-write the integral as a sum

$\displaystyle \mu_{n+m}(x, z) = \sum_{y \in Y} \mu_m(x, y)\mu_n(y, z)$

which we recognise as a restatement of how Markov transition matrices combine.

# Some Examples

## A Fully Deterministic System

A deterministic system can be formulated as a Markov process with a particularly simple transition kernel given by

$\displaystyle \mu_t(x_s, A) = \delta(f_t(x_s), A) \triangleq \begin{cases} 1 & \text{if } f_t(x_s) \in A \\ 0 & \text{if } f_t(x_s) \notin A \end{cases}$

where $f_t$ is the deterministic state update function (the flow) and $\delta$ is the Dirac delta function.

## Parameters

Let us suppose that the determinstic system is dependent on some time-varying values for which we we are unable or unwish to specify a deterministic model. For example, we may be considering predator-prey model where the parameters cannot explain every aspect. We could augment the deterministic kernel in the previous example with

$\displaystyle \mu_t(\theta_s, {\mathrm{d}\phi}) = {{\cal{N}} \left( {\mathrm{d}\phi} \,\vert\, \theta_s, \tau^2(t-s) \right)}$

where we use Greek letters for the parameters (and Roman letters for state) and we use e.g. ${\mathrm{d}\phi}$ to indicate probability densities. In other words that the parameters tend to wiggle around like Brown’s pollen particles rather than remaining absolutely fixed.

## Ornstein-Uhlenbeck

Of course Brownian motion or diffusion may not be a good model for our parameters; with Brownian motion, the parameters could drift off to $\pm\infty$. We might believe that our parameters tend to stay close to some given value (mean-reverting) and use the Ornstein-Uhlenbeck kernel.

$\displaystyle \mu_t(\theta_s, {\mathrm{d}\phi}) = {{\cal{N}} \left( {\mathrm{d}\phi} \,\vert\, \alpha + (\theta_s - \alpha)e^{-\beta t},\frac{\sigma^2}{2\beta}\big(1 - e^{-2\beta t}\big) \right)}$

where $\beta$ expresses how strongly we expect the parameter to respond to perturbations, $\alpha$ is the mean to which the process wants to revert (aka the asymptotic mean) and $\sigma^2$ expresses how noisy the process is.

It is sometimes easier to view these transition kernels in terms of stochastic differential equations. Brownian motion can be expressed as

$\displaystyle \mathrm{d}X_t = \sigma\mathrm{d}W_t$

and Ornstein-Uhlenbeck can be expressed as

$\displaystyle \mathrm{d}X_t = -\beta(X_t - \alpha)\mathrm{d}t + \sigma\mathrm{d}W_t$

where $W_t$ is the Wiener process.

Let us check that the latter stochastic differential equation gives the stated kernel. Re-writing it in integral form and without loss of generality taking $s= 0$

$\displaystyle X_t = \alpha + (x_0 - \alpha)e^{-\beta t} + \sigma\int_0^t e^{-\beta(t - s)}\mathrm{d}W_s$

Since the integral is of a deterministic function, the distribution of $X_t$ is normal. Thus we need only calculate the mean and variance.

The mean is straightforward.

$\displaystyle \mathbb{E}[X_t \,\vert\, X_0 = x_0] = \mathbb{E}\Bigg[\alpha + (x_0 - \alpha)e^{-\beta t} + \sigma\int_0^t e^{-\beta(t - s)}\mathrm{d}W_s \,\vert\, X_0 = x_0\Bigg] = \alpha + (x_0 - \alpha)e^{-\beta t}$

Without loss of generality assume $t \leq u$ and writing $\mathbb{C}$ for covariance

\displaystyle \begin{aligned} \mathbb{C}[X_u, X_t \,\vert\, X_0 = x_0] &= \mathbb{E}\Bigg[\Bigg( \sigma\int_0^u e^{-\beta(u - s)}\mathrm{d}W_s \Bigg) \Bigg( \sigma\int_0^t e^{-\beta(t - s)}\mathrm{d}W_s \Bigg)\Bigg] \\ &= \sigma^2e^{-\beta(u + t)} \mathbb{E}\Bigg[\Bigg(\int_0^u e^{\beta s}\mathrm{d}W_s\Bigg) \Bigg(\int_0^t e^{\beta s}\mathrm{d}W_s\Bigg)\Bigg] \\ &= \sigma^2e^{-\beta(u + t)} \mathbb{E}\Bigg[\Bigg(\int_0^t e^{\beta s}\mathrm{d}W_s + \int_t^u e^{\beta s}\mathrm{d}W_s\Bigg) \Bigg(\int_0^t e^{\beta s}\mathrm{d}W_s\Bigg)\Bigg] \end{aligned}

And now we can use Ito and independence

\displaystyle \begin{aligned} \mathbb{C}[X_u, X_t \,\vert\, X_0 = x_0] &= \sigma^2e^{-\beta(u + t)}\mathbb{E}\Bigg[ \int_0^t e^{2\beta s}\mathrm{d}s \Bigg] \\ &= \frac{\sigma^2e^{-\beta(u + t)}}{2\beta}\big(e^{2\beta t} - 1\big) \end{aligned}

Substituting in $t = u$ gives the desired result.

# Bibliography

Kallenberg, O. 2002. Foundations of Modern Probability. Probability and Its Applications. Springer New York. http://books.google.co.uk/books?id=TBgFslMy8V4C.

Rogers, L. C. G., and David Williams. 2000. Diffusions, Markov Processes, and Martingales. Vol. 1. Cambridge Mathematical Library. Cambridge: Cambridge University Press.

# Population Growth Estimation via Hamiltonian Monte Carlo

Here’s the same analysis of estimating population growth using Stan.

data {
int<lower=0> N; // number of observations
vector[N] y;    // observed population
}

parameters {
real r;
}

model {
real k;
real p0;
real deltaT;
real sigma;
real mu0;
real sigma0;
vector[N] p;
k      <- 1.0;
p0     <- 0.1;
deltaT <- 0.0005;
sigma  <- 0.01;
mu0    <- 5;
sigma0 <- 10;

r ~ normal(mu0, sigma0);

for (n in 1:N) {
p[n] <- k * p0 * exp((n - 1) * r * deltaT) / (k + p0 * (exp((n - 1) * r * deltaT) - 1));
y[n] ~ normal(p[n], sigma);
}
}

Empirically, by looking at the posterior, this seems to do a better job than either extended Kalman or vanilla Metropolis.

# Introduction

Let us see if we can estimate the parameter for population growth using MCMC in the example in which we used Kalman filtering.

We recall the model.

\displaystyle \begin{aligned} \dot{p} & = rp\Big(1 - \frac{p}{k}\Big) \end{aligned}

$\displaystyle p = \frac{kp_0\exp rt}{k + p_0(\exp rt - 1)}$

And we are allowed to sample at regular intervals

\displaystyle \begin{aligned} p_i &= \frac{kp_0\exp r\Delta T i}{k + p_0(\exp r\Delta T i - 1)} \\ y_i &= p_i + \epsilon_i \end{aligned}

In other words $y_i \sim {\cal{N}}(p_i, \sigma^2)$, where $\sigma$ is known so the likelihood is

$\displaystyle p(y\,|\,r) \propto \prod_{i=1}^n \exp{\bigg( -\frac{(y_i - p_i)^2}{2\sigma^2}\bigg)} = \exp{\bigg( -\sum_{i=1}^n \frac{(y_i - p_i)^2}{2\sigma^2}\bigg)}$

Let us assume a prior of $r \sim {\cal{N}}(\mu_0,\sigma_0^2)$ then the posterior becomes

$\displaystyle p(r\,|\,y) \propto \exp{\bigg( -\frac{(r - \mu_0)^2}{2\sigma_0^2} \bigg)} \exp{\bigg( -\sum_{i=1}^n \frac{(y_i - p_i)^2}{2\sigma^2}\bigg)}$

## Preamble

> {-# OPTIONS_GHC -Wall                      #-}
> {-# OPTIONS_GHC -fno-warn-type-defaults    #-}
> {-# OPTIONS_GHC -fno-warn-unused-do-bind   #-}
> {-# OPTIONS_GHC -fno-warn-missing-methods  #-}
> {-# OPTIONS_GHC -fno-warn-orphans          #-}

> {-# LANGUAGE NoMonomorphismRestriction     #-}

> module PopGrowthMCMC where
>
> import qualified Data.Vector.Unboxed as V
> import Data.Random.Source.PureMT
> import Data.Random
> import Data.Histogram.Fill
> import Data.Histogram.Generic ( Histogram )


# Implementation

We assume most of the parameters are known with the exception of the the growth rate $r$. We fix this also in order to generate test data.

> k, p0 :: Double
> k = 1.0
> p0 = 0.1

> r, deltaT :: Double
> r = 10.0
> deltaT = 0.0005

> nObs :: Int
> nObs = 150


Here’s the implementation of the logistic function

> logit :: Double -> Double -> Double -> Double
> logit p0 k x = k * p0 * (exp x) / (k + p0 * (exp x - 1))


Let us create some noisy data.

> sigma :: Double
> sigma = 1e-2

> samples :: [Double]
> samples = zipWith (+) mus epsilons
>   where
>     mus = map (logit p0 k . (* (r * deltaT))) (map fromIntegral [0..])
>     epsilons = evalState (sample $replicateM nObs$ rvar (Normal 0.0 sigma)) (pureMT 3)


Arbitrarily let us set the prior to have a rather vague normal distribution.

> mu0, sigma0 :: Double
> mu0 = 5.0
> sigma0 = 1e1

> prior :: Double -> Double
> prior r = exp (-(r - mu0)**2 / (2 * sigma0**2))

> likelihood :: Double -> [Double] -> Double
> likelihood r ys = exp (-sum (zipWith (\y mu -> (y - mu)**2 / (2 * sigma**2)) ys mus))
>   where
>     mus :: [Double]
>     mus = map (logit p0 k . (* (r * deltaT))) (map fromIntegral [0..])

> posterior :: Double -> [Double] -> Double
> posterior r ys = likelihood r ys * prior r


The Metropolis algorithm tells us that we always jump to a better place but only sometimes jump to a worse place. We count the number of acceptances as we go.

> acceptanceProb' :: Double -> Double -> [Double] -> Double
> acceptanceProb' r r' ys = min 1.0 ((posterior r' ys) / (posterior r ys))

> oneStep :: (Double, Int) -> (Double, Double) -> (Double, Int)
> oneStep (r, nAccs) (proposedJump, acceptOrReject) =
>   if acceptOrReject < acceptanceProb' r (r + proposedJump) samples
>   then (r + proposedJump, nAccs + 1)
>   else (r, nAccs)


Here are our proposals.

> normalisedProposals :: Int -> Double -> Int -> [Double]
> normalisedProposals seed sigma nIters =
>   evalState (replicateM nIters (sample (Normal 0.0 sigma)))
>   (pureMT $fromIntegral seed)  We also need samples from the uniform distribution > acceptOrRejects :: Int -> Int -> [Double] > acceptOrRejects seed nIters = > evalState (replicateM nIters (sample stdUniform)) > (pureMT$ fromIntegral seed)


Now we can actually run our simulation. We set the number of jumps and a burn in but do not do any thinning.

> nIters, burnIn :: Int
> nIters = 100000
> burnIn = nIters div 10


Let us start our chain to the mean of the prior. In theory this shoudn’t matter as by the time we have burnt in we should be sampling in the high density region of the distribution.

> startMu :: Double
> startMu = 5.0


This jump size should allow us to sample the region of high density at a reasonable granularity.

> jumpVar :: Double
> jumpVar = 0.01


Now we can test our MCMC implementation.

> test :: Int -> [(Double, Int)]
> test seed =
>   drop burnIn $> scanl oneStep (startMu, 0)$
>   zip (normalisedProposals seed jumpVar nIters)
>       (acceptOrRejects seed nIters)


We put the data into a histogram.

> numBins :: Int
> numBins = 400

> hb :: HBuilder Double (Data.Histogram.Generic.Histogram V.Vector BinD Double)
> hb = forceDouble -<< mkSimple (binD lower numBins upper)
>   where
>     lower = r - 0.5 * sigma0
>     upper = r + 0.5 * sigma0
>
> hist :: Int -> Histogram V.Vector BinD Double
> hist seed = fillBuilder hb (map fst $test seed)  With 50 observations we don’t seem to be very certain about the growth rate. With 100 observations we do very much better. And with 150 observations we do even better. # The Flow of the Thames: An Autoregressive Model # Thames Flux It is roughly 150 miles from the source of the Thames to Kingston Bridge. If we assume that it flows at about 2 miles per hour then the water at Thames Head will have reached Kingston very roughly at $\frac{150}{24\times 2} \approxeq 3$ days. The Environmental Agency measure the flux at Kingston Bridge on a twice daily basis. Can we predict this? In the first instance without any other data and using our observation that Thames flushes itself every 3 days, let us try $\displaystyle X_t = \theta_1 X_{t-1} + \theta_2 X_{t-2} + \theta_3 X_{t-3} + \epsilon_t$ where $X_t$ is the flux on day $t$ and $\{\epsilon_t\}_{t \in \mathbb{N}}$ are independent normal errors with mean $0$ and variance some given value $\sigma^2$. # Kalman As it stands, our model is not Markov so we cannot directly apply techniques such as Kalman filtering or particle filtering to estimate the parameters. However we can re-write the model as $\displaystyle \begin{bmatrix} \theta_1^{(t)} \\ \theta_2^{(t)} \\ \theta_3^{(t)} \end{bmatrix} = \begin{bmatrix} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{bmatrix} \begin{bmatrix} \theta_1^{(t-1)} \\ \theta_2^{(t-1)} \\ \theta_3^{(t-1)} \end{bmatrix} + \begin{bmatrix} \eta_{t} \\ \eta_{t} \\ \eta_{t} \end{bmatrix}$ $\displaystyle y_t = \begin{bmatrix} x_{t-1} & x_{t-2} & x_{t-3} \end{bmatrix} \begin{bmatrix} \theta_1^{(t)} \\ \theta_2^{(t)} \\ \theta_3^{(t)} \end{bmatrix} + \epsilon_{t}$ Note that the observation map now varies over time so we have modify our Kalman filter implementation to accept a different matrix at each step. > {-# OPTIONS_GHC -Wall #-} > {-# OPTIONS_GHC -fno-warn-name-shadowing #-} > {-# OPTIONS_GHC -fno-warn-type-defaults #-} > {-# OPTIONS_GHC -fno-warn-unused-do-bind #-} > {-# OPTIONS_GHC -fno-warn-missing-methods #-} > {-# OPTIONS_GHC -fno-warn-orphans #-}  > {-# LANGUAGE DataKinds #-} > {-# LANGUAGE ScopedTypeVariables #-} > {-# LANGUAGE RankNTypes #-} > {-# LANGUAGE TypeOperators #-} > {-# LANGUAGE TypeFamilies #-}  > module Autoregression ( > predictions > ) where  > import GHC.TypeLits > import Numeric.LinearAlgebra.Static > import Data.Maybe ( fromJust )  > import qualified Data.Vector as V  > inv :: (KnownNat n, (1 <=? n) ~ 'True) => Sq n -> Sq n > inv m = fromJust$ linSolve m eye

> outer ::  forall m n . (KnownNat m, KnownNat n,
>                         (1 <=? n) ~ 'True, (1 <=? m) ~ 'True) =>
>           R n -> Sq n -> [L m n] -> Sq m -> Sq n -> Sq n -> [R m] -> [(R n, Sq n)]
> outer muPrior sigmaPrior bigHs bigSigmaY bigA bigSigmaX ys = result
>   where
>     result = scanl update (muPrior, sigmaPrior) (zip ys bigHs)
>
>     update :: (R n, Sq n) -> (R m, L m n) -> (R n, Sq n)
>     update (xHatFlat, bigSigmaHatFlat) (y, bigH) =
>       (xHatFlatNew, bigSigmaHatFlatNew)
>       where
>         v :: R m
>         v = y - bigH #> xHatFlat
>         bigS :: Sq m
>         bigS = bigH  bigSigmaHatFlat  (tr bigH) + bigSigmaY
>         bigK :: L n m
>         bigK = bigSigmaHatFlat  (tr bigH)  (inv bigS)
>         xHat :: R n
>         xHat = xHatFlat + bigK #> v
>         bigSigmaHat :: Sq n
>         bigSigmaHat = bigSigmaHatFlat - bigK  bigS  (tr bigK)
>         xHatFlatNew :: R n
>         xHatFlatNew = bigA #> xHat
>         bigSigmaHatFlatNew :: Sq n
>         bigSigmaHatFlatNew = bigA  bigSigmaHat  (tr bigA) + bigSigmaX


We can now set up the parameters to run the filter.

> stateVariance :: Double
> stateVariance = 1e-8

> bigSigmaX :: Sq 3
> bigSigmaX = fromList [ stateVariance, 0.0,           0.0
>                      , 0.0,           stateVariance, 0.0
>                      , 0.0,           0.0,           stateVariance
>                      ]

> bigA :: Sq 3
> bigA = eye

> muPrior :: R 3
> muPrior = fromList [0.0, 0.0, 0.0]

> sigmaPrior :: Sq 3
> sigmaPrior = fromList [ 1e1, 0.0, 0.0
>                       , 0.0, 1e1, 0.0
>                       , 0.0, 0.0, 1e1
>                       ]

> bigHsBuilder :: V.Vector Double -> [L 1 3]
> bigHsBuilder flows =
>   V.toList $> V.zipWith3 (\x0 x1 x2 -> fromList [x0, x1, x2]) > (V.tail flows) (V.tail$ V.tail flows) (V.tail $V.tail$ V.tail flows)

> obsVariance :: Double
> obsVariance = 1.0e-2

> bigSigmaY :: Sq 1
> bigSigmaY = fromList [ obsVariance ]

> predict :: R 3 -> Double -> Double -> Double -> Double
> predict theta f1 f2 f3 = h1 * f1 + h2 * f2 + h3 * f3
>   where
>     (h1, t1) = headTail theta
>     (h2, t2) = headTail t1
>     (h3, _)  = headTail t2

> thetas :: V.Vector Double -> [(R 3, Sq 3)]
> thetas flows = outer muPrior sigmaPrior (bigHsBuilder flows)
>                bigSigmaY bigA bigSigmaX (map (fromList . return) (V.toList flows))

> predictions :: V.Vector Double -> V.Vector Double
> predictions flows =
>   V.zipWith4 predict
>   (V.fromList $map fst (thetas flows)) > flows (V.tail flows) (V.tail$ V.tail flows)


# How Good is Our Model?

If we assume that parameters are essentially fixed by taking the state variance to be e.g. $10^{-8}$ then the fit is not good.

However, if we assume the parameters to undergo Brownian motion by taking the state variance to be e.g. $10^{-2}$ then we get a much better fit. Of course, Brownian motion is probably not a good way of modelling the parameters; we hardly expect that these could wander off to infinity.

# Introduction

Suppose you want to sample from the truncated normal distribution. One way to do this is to use rejection sampling. But if you do this naïvely then you will run into performance problems. The excellent Devroye (1986) who references Marsaglia (1964) gives an efficient rejection sampling scheme using the Rayleigh distribution. The random-fu package uses the Exponential distribution.

# Performance

> {-# OPTIONS_GHC -Wall                     #-}
> {-# OPTIONS_GHC -fno-warn-type-defaults   #-}
> {-# OPTIONS_GHC -fno-warn-unused-do-bind  #-}
> {-# OPTIONS_GHC -fno-warn-missing-methods #-}
> {-# OPTIONS_GHC -fno-warn-orphans         #-}

> {-# LANGUAGE FlexibleContexts             #-}

> import Control.Monad
> import Data.Random
> import qualified Data.Random.Distribution.Normal as N

> import Data.Random.Source.PureMT


Here’s the naïve implementation.

> naiveReject :: Double -> RVar Double
> naiveReject x = doit
>   where
>     doit = do
>       y <- N.stdNormal
>       if y < x
>         then doit
>         else return y


And here’s an implementation using random-fu.

> expReject :: Double -> RVar Double
> expReject x = N.normalTail x


Let’s try running both of them

> n :: Int
> n = 10000000

> lower :: Double
> lower = 2.0

> testExp :: [Double]
> testExp = evalState (replicateM n $sample (expReject lower)) (pureMT 3)  > testNaive :: [Double] > testNaive = evalState (replicateM n$ sample (naiveReject lower)) (pureMT 3)

> main :: IO ()
> main = do
>   print $sum testExp > print$ sum testNaive


Let’s try building and running both the naïve and better tuned versions.

ghc -O2 CompareRejects.hs

As we can see below we get 59.98s and 4.28s, a compelling reason to use the tuned version. And the difference in performance will get worse the less of the tail we wish to sample from.

## Tuned

2.3731610476911187e7
11,934,195,432 bytes allocated in the heap
5,257,328 bytes copied during GC
44,312 bytes maximum residency (2 sample(s))
21,224 bytes maximum slop
1 MB total memory in use (0 MB lost due to fragmentation)

Tot time (elapsed)  Avg pause  Max pause
Gen  0     23145 colls,     0 par    0.09s    0.11s     0.0000s    0.0001s
Gen  1         2 colls,     0 par    0.00s    0.00s     0.0001s    0.0002s

INIT    time    0.00s  (  0.00s elapsed)
MUT     time    4.19s  (  4.26s elapsed)
GC      time    0.09s  (  0.11s elapsed)
EXIT    time    0.00s  (  0.00s elapsed)
Total   time    4.28s  (  4.37s elapsed)

%GC     time       2.2%  (2.6% elapsed)

Alloc rate    2,851,397,967 bytes per MUT second

Productivity  97.8% of total user, 95.7% of total elapsed

## Naïve

2.3732073159369867e7
260,450,762,656 bytes allocated in the heap
111,891,960 bytes copied during GC
85,536 bytes maximum residency (2 sample(s))
76,112 bytes maximum slop
1 MB total memory in use (0 MB lost due to fragmentation)

Tot time (elapsed)  Avg pause  Max pause
Gen  0     512768 colls,     0 par    1.86s    2.24s     0.0000s    0.0008s
Gen  1         2 colls,     0 par    0.00s    0.00s     0.0001s    0.0002s

INIT    time    0.00s  (  0.00s elapsed)
MUT     time   58.12s  ( 58.99s elapsed)
GC      time    1.86s  (  2.24s elapsed)
EXIT    time    0.00s  (  0.00s elapsed)
Total   time    59.98s  ( 61.23s elapsed)

%GC     time       3.1%  (3.7% elapsed)

Alloc rate    4,481,408,869 bytes per MUT second

Productivity  96.9% of total user, 94.9% of total elapsed

# Bibliography

Devroye, L. 1986. Non-Uniform Random Variate Generation. Springer-Verlag. http://books.google.co.uk/books?id=mEw\_AQAAIAAJ.

Marsaglia, G. 1964. “Generating a Variable from the Tail of the Normal Distribution.” J-Technometrics 6 (1): 101–2.

# Introduction

Simple models for e.g. financial option pricing assume that the volatility of an index or a stock is constant, see here for example. However, simple observation of time series show that this is not the case; if it were then the log returns would be white noise

One approach which addresses this, GARCH (Generalised AutoRegressive Conditional Heteroskedasticity), models the evolution of volatility deterministically.

Stochastic volatility models treat the volatility of a return on an asset, such as an option to buy a security, as a Hidden Markov Model (HMM). Typically, the observable data consist of noisy mean-corrected returns on an underlying asset at equally spaced time points.

There is evidence that Stochastic Volatility models (Kim, Shephard, and Chib (1998)) offer increased flexibility over the GARCH family, e.g. see Geweke (1994), Fridman and Harris (1998) and Jacquier, Polson, and Rossi (1994). Despite this and judging by the numbers of questions on the R Special Interest Group on Finance mailing list, the use of GARCH in practice far outweighs that of Stochastic Volatility. Reasons cited are the multiplicity of estimation methods for the latter and the lack of packages (but see here for a recent improvement to the paucity of packages).

In their tutorial on particle filtering, Doucet and Johansen (2011) give an example of stochastic volatility. We save this approach for future blog posts and follow Lopes and Polson and the excellent lecture notes by Hedibert Lopes.

Here’s the model.

\displaystyle \begin{aligned} H_0 &\sim {\mathcal{N}}\left( m_0, C_0\right) \\ H_t &= \mu + \phi H_{t-1} + \tau \eta_t \\ Y_n &= \beta \exp(H_t / 2) \epsilon_n \\ \end{aligned}

We wish to estimate $\mu, \phi, \tau$ and $\boldsymbol{h}$. To do this via a Gibbs sampler we need to sample from

$\displaystyle {p \left( \mu, \phi, \tau \,\vert\, \boldsymbol{h}, \boldsymbol{y} \right)} \quad \text{and} \quad {p \left( \boldsymbol{h} \,\vert\, \mu, \phi, \tau, \boldsymbol{y} \right)}$

> {-# OPTIONS_GHC -Wall                      #-}
> {-# OPTIONS_GHC -fno-warn-type-defaults    #-}
> {-# OPTIONS_GHC -fno-warn-unused-do-bind   #-}
> {-# OPTIONS_GHC -fno-warn-missing-methods  #-}
> {-# OPTIONS_GHC -fno-warn-orphans          #-}

> {-# LANGUAGE RecursiveDo                   #-}
> {-# LANGUAGE ExplicitForAll                #-}
> {-# LANGUAGE TypeOperators                 #-}
> {-# LANGUAGE TypeFamilies                  #-}
> {-# LANGUAGE ScopedTypeVariables           #-}
> {-# LANGUAGE DataKinds                     #-}
> {-# LANGUAGE FlexibleContexts              #-}

> module StochVol (
>     bigM
>   , bigM0
>   , runMC
>   , ys
>   , vols
>   , expectationTau2
>   , varianceTau2
>   ) where

> import Numeric.LinearAlgebra.HMatrix hiding ( (===), (|||), Element,
>                                               (<>), (#>), inv )
> import qualified Numeric.LinearAlgebra.Static as S
> import Numeric.LinearAlgebra.Static ( (<>) )
> import GHC.TypeLits
> import Data.Proxy
> import Data.Maybe ( fromJust )

> import Data.Random
> import Data.Random.Source.PureMT
> import Control.Monad.Writer hiding ( (<>) )
> import Control.Applicative

> import qualified Data.Vector as V

> inv :: (KnownNat n, (1 <=? n) ~ 'True) => S.Sq n -> S.Sq n
> inv m = fromJust S.linSolve m S.eye  > infixr 8 #> > (#>) :: (KnownNat m, KnownNat n) => S.L m n -> S.R n -> S.R m > (#>) = (S.#>)  > type StatsM a = RVarT (Writer [((Double, Double), Double)]) a  > (|||) :: (KnownNat ((+) r1 r2), KnownNat r2, KnownNat c, KnownNat r1) => > S.L c r1 -> S.L c r2 -> S.L c ((+) r1 r2) > (|||) = (S.¦)  # Marginal Distribution for Parameters Let us take a prior that is standard for linear regression $\displaystyle (\boldsymbol{\theta}, \tau^2) \sim {\mathcal{NIG}}(\boldsymbol{\theta}_0, V_0, \nu_0, s_0^2)$ where $\boldsymbol{\theta} = (\mu, \phi)^\top$ and use standard results for linear regression to obtain the required marginal distribution. That the prior is Normal Inverse Gamma (${\cal{NIG}}$) means \displaystyle \begin{aligned} \boldsymbol{\theta} \, | \, \tau^2 & \sim {\cal{N}}(\boldsymbol{\theta}_0, \tau^2 V_0) \\ \tau^2 & \sim {\cal{IG}}(\nu_0 / 2, \nu_0 s_0^2 / 2) \end{aligned} Standard Bayesian analysis for regression tells us that the (conditional) posterior distribution for $\displaystyle y_i = \beta + \alpha x_i + \epsilon_i$ where the $\{\epsilon_i\}$ are IID normal with variance $\sigma^2$ is given by $\displaystyle {p \left( \alpha, \beta, \eta \,\vert\, \boldsymbol{y}, \boldsymbol{x} \right)} = {\cal{N}}((\alpha, \beta); \mu_n, \sigma^2\Lambda_n^{-1})\,{\cal{IG}}(a_n, b_n)$ with $\displaystyle \Lambda_n = X_n^\top X_n + \Lambda_0$ $\displaystyle \begin{matrix} \mu_n = \Lambda_n^{-1}({X_n}^{\top}{X_n}\hat{\boldsymbol{\beta}}_n + \Lambda_0\mu_0) & \textrm{where} & \hat{\boldsymbol\beta}_n = ({X}_n^{\rm T}{X}_n)^{-1}{X}_n^{\rm T}\boldsymbol{y}_n \end{matrix}$ $\displaystyle \begin{matrix} a_n = \frac{n}{2} + a_0 & \quad & b_n = b_0 + \frac{1}{2}(\boldsymbol{y}^\top\boldsymbol{y} + \boldsymbol{\mu}_0^\top\Lambda_0\boldsymbol{\mu}_0 - \boldsymbol{\mu}_n^\top\Lambda_n\boldsymbol{\mu}_n) \end{matrix}$ ## Recursive Form We can re-write the above recursively. We do not need to for this blog article but it will be required in any future blog article which uses Sequential Monte Carlo techniques. $\displaystyle \Lambda_n = \boldsymbol{x}_n^\top \boldsymbol{x}_n + \Lambda_{n-1}$ Furthermore $\displaystyle \Lambda_{n}\mu_{n} = {X}_{n}^{\rm T}\boldsymbol{y}_{n} + \Lambda_0\mu_0 = {X}_{n-1}^{\rm T}\boldsymbol{y}_{n-1} + \boldsymbol{x}_n^\top y_n + \Lambda_0\mu_0 = \Lambda_{n-1}\mu_{n-1} + \boldsymbol{x}_n^\top y_n$ so we can write $\displaystyle \boldsymbol{\mu}_n = \Lambda_n^{-1}(\Lambda_{n-1}\mu_{n-1} + \boldsymbol{x}_n^\top y_n)$ and $\displaystyle \begin{matrix} a_n = a_{n-1} + \frac{1}{2} & \quad & b_n = b_{n-1} + \frac{1}{2}\big[(y_n - \boldsymbol{\mu}_n^\top \boldsymbol{x}_n)y_n + (\boldsymbol{\mu}_{n-1} - \boldsymbol{\mu}_{n})^\top \Lambda_{n-1}\boldsymbol{\mu}_{n-1}\big] \end{matrix}$ ## Specialising In the case of our model we can specialise the non-recursive equations as $\displaystyle \Lambda_n = \begin{bmatrix} 1 & 1 & \ldots & 1 \\ x_1 & x_2 & \ldots & x_n \end{bmatrix} \begin{bmatrix} 1 & x_1 \\ 1 & x_2 \\ \ldots & \ldots \\ 1 & x_n \end{bmatrix} + \Lambda_0$ $\displaystyle \begin{matrix} \mu_n = (\Lambda_n)^{-1}({X_n}^{\top}{X_n}\hat{\boldsymbol{\beta}}_n + \Lambda_0\mu_0) & \textrm{where} & \hat{\boldsymbol\beta}_n = ({X}_n^{\rm T}{X}_n)^{-1}{X}_n^{\rm T}\boldsymbol{x}_{1:n} \end{matrix}$ $\displaystyle \begin{matrix} a_n = \frac{n}{2} + a_0 & \quad & b_n = b_0 + \frac{1}{2}(\boldsymbol{x}_{1:n}^\top\boldsymbol{x}_{1:n} + \boldsymbol{\mu}_0^\top\Lambda_0\boldsymbol{\mu}_0 - \boldsymbol{\mu}_n^\top\Lambda_n\boldsymbol{\mu}_n) \end{matrix}$ Let’s re-write the notation to fit our model. $\displaystyle \Lambda_n = \begin{bmatrix} 1 & 1 & \ldots & 1 \\ h_1 & h_2 & \ldots & h_n \end{bmatrix} \begin{bmatrix} 1 & h_1 \\ 1 & h_2 \\ \ldots & \ldots \\ 1 & h_n \end{bmatrix} + \Lambda_0$ $\displaystyle \begin{matrix} \mu_n = (\Lambda_n)^{-1}({H_n}^{\top}{H_n}\hat{\boldsymbol{\theta}}_n + \Lambda_0\mu_0) & \textrm{where} & \hat{\boldsymbol\theta}_n = ({H}_n^{\rm T}{H}_n)^{-1}{H}_n^{\rm T}\boldsymbol{x}_{1:n} \end{matrix}$ $\displaystyle \begin{matrix} a_n = \frac{n}{2} + a_0 & \quad & b_n = b_0 + \frac{1}{2}(\boldsymbol{x}_{1:n}^\top\boldsymbol{x}_{1:n} + \boldsymbol{\mu}_0^\top\Lambda_0\boldsymbol{\mu}_0 - \boldsymbol{\mu}_n^\top\Lambda_n\boldsymbol{\mu}_n) \end{matrix}$ Sample from ${p \left( \boldsymbol{\theta}, \tau^2 \,\vert\, \boldsymbol{h}, \boldsymbol{y} \right)} \sim {\mathcal{NIG}}(\boldsymbol{\theta}_1, V_1, \nu_1, s_1^2)$ We can implement this in Haskell as > sampleParms :: > forall n m . > (KnownNat n, (1 <=? n) ~ 'True) => > S.R n -> S.L n 2 -> S.R 2 -> S.Sq 2 -> Double -> Double -> > RVarT m (S.R 2, Double) > sampleParms y bigX theta_0 bigLambda_0 a_0 s_02 = do > let n = natVal (Proxy :: Proxy n) > a_n = 0.5 * (a_0 + fromIntegral n) > bigLambda_n = bigLambda_0 + (tr bigX) <> bigX > invBigLambda_n = inv bigLambda_n > theta_n = invBigLambda_n #> ((tr bigX) #> y + (tr bigLambda_0) #> theta_0) > b_0 = 0.5 * a_0 * s_02 > b_n = b_0 + > 0.5 * (S.extract (S.row y <> S.col y)!0!0) + > 0.5 * (S.extract (S.row theta_0 <> bigLambda_0 <> S.col theta_0)!0!0) - > 0.5 * (S.extract (S.row theta_n <> bigLambda_n <> S.col theta_n)!0!0) > g <- rvarT (Gamma a_n (recip b_n)) > let s2 = recip g > invBigLambda_n' = m <> invBigLambda_n > where > m = S.diag S.vector (replicate 2 s2)
>   m1 <- rvarT StdNormal
>   m2 <- rvarT StdNormal
>   let theta_n' :: S.R 2
>       theta_n' = theta_n + S.chol (S.sym invBigLambda_n') #> (S.vector [m1, m2])
>   return (theta_n', s2)


# Marginal Distribution for State

## Marginal for $H_0$

Using a standard result about conjugate priors and since we have

$\displaystyle h_0 \sim {\cal{N}}(m_0,C_0) \quad h_1 \vert h_0 \sim {\cal{N}}(\mu + \phi h_0, \tau^2)$

we can deduce

$\displaystyle h_0 \vert h_1 \sim {\cal{N}}(m_1,C_1)$

where

\displaystyle \begin{aligned} \frac{1}{C_1} &= \frac{1}{C_0} + \frac{\phi^2}{\tau^2} \\ \frac{m_1}{C_1} &= \frac{m_0}{C_0} + \frac{\phi(h_1 - \mu)}{\tau^2} \end{aligned}

> sampleH0 :: Double ->
>             Double ->
>             V.Vector Double ->
>             Double ->
>             Double ->
>             Double ->
>             RVarT m Double
> sampleH0 iC0 iC0m0 hs mu phi tau2 = do
>   let var = recip (iC0 + phi^2 / tau2) > mean = var * (iC0m0 + phi * ((hs V.! 0) - mu) / tau2) > rvarT (Normal mean (sqrt var))  ## Marginal for $H_1 \ldots H_n$ From the state equation, we have \displaystyle \begin{aligned} H_{t+1} &= \mu + \phi H_{t} + \tau \eta_t \\ \phi^2 H_{t} &= -\phi\mu + \phi H_{t+1} - \phi \tau \eta_t \\ \end{aligned} We also have \displaystyle \begin{aligned} H_{t} &= \mu + \phi H_{t-1} + \tau \eta_{t-1} \\ \end{aligned} Adding the two expressions together gives \displaystyle \begin{aligned} (1 + \phi^2)H_{t} &= \phi (H_{t-1} + H_{t+1}) + \mu (1 - \phi) + \tau(\eta_{t-1} - \phi\eta_t) \\ H_{t} &= \frac{\phi}{1 + \phi^2} (H_{t-1} + H_{t+1}) + \mu \frac{1 - \phi}{1 + \phi^2} + \frac{\tau}{1 + \phi^2}(\eta_{t-1} - \phi\eta_t) \\ \end{aligned} Since $\{\eta_t\}$ are standard normal, then $H_t$ conditional on $H_{t-1}$ and $H_{t+1}$ is normally distributed, and \displaystyle \begin{aligned} \mathbb{E}(H_n\mid H_{n-1}, H_{n+1}) &= \frac{1 - \phi}{1+\phi^2}\mu + \frac{\phi}{1+\phi^2}(H_{n-1} + H_{n+1}) \\ \mathbb{V}(H_n\mid H_{n-1}, H_{n+1}) &= \frac{\tau^2}{1+\phi^2} \end{aligned} We also have $\displaystyle h_{n+1} \vert h_n \sim {\cal{N}}(\mu + \phi h_n, \tau^2)$ Writing $\displaystyle \boldsymbol{h}_{-t} = \begin{bmatrix} h_0, & h_1, & \ldots, & h_{t-1}, & h_{t+1}, & \ldots, & h_{n-1}, & h_n \end{bmatrix}$ by Bayes’ Theorem we have $\displaystyle {p \left( h_t \,\vert\, \boldsymbol{h}_{-t}, \theta, \boldsymbol{y} \right)} \propto {p \left( y_t \,\vert\, h_t \right)} {p \left( h_t \,\vert\, \boldsymbol{h}_{-t}, \theta, y_{-t} \right)} = f_{\cal{N}}(y_t;0,e^{h_t}) f_{\cal{N}}(h_t;\mu_t,\nu_t^2)$ where $f_{\cal{N}}(x;\mu,\sigma^2)$ is the probability density function of a normal distribution. We can sample from this using Metropolis 1. For each $t$, sample $h_t^\flat$ from ${\cal{N}}(h_t, \gamma^2)$ where $\gamma^2$ is the tuning variance. 2. For each $t=1, \ldots, n$, compute the acceptance probability $\displaystyle p_t = \min{\Bigg(\frac{f_{\cal{N}}(h^\flat_t;\mu_t,\nu_t^2) f_{\cal{N}}(y_t;0,e^{h^\flat_t})}{f_{\cal{N}}(h_t;\mu_t,\nu_t^2) f_{\cal{N}}(y_t;0,e^{h_t})}, 1 \Bigg)}$ 1. For each $t$, compute a new value of $h_t$ $\displaystyle h^\sharp_t = \begin{cases} h^\flat_t \text{with probability } p_t \\ h_t \text{with probability } 1 - p_t \end{cases}$ > metropolis :: V.Vector Double -> > Double -> > Double -> > Double -> > Double -> > V.Vector Double -> > Double -> > RVarT m (V.Vector Double) > metropolis ys mu phi tau2 h0 hs vh = do > let eta2s = V.replicate (n-1) (tau2 / (1 + phi^2)) V.snoc tau2 > etas = V.map sqrt eta2s > coef1 = (1 - phi) / (1 + phi^2) * mu > coef2 = phi / (1 + phi^2) > mu_n = mu + phi * (hs V.! (n-1)) > mu_1 = coef1 + coef2 * ((hs V.! 1) + h0) > innerMus = V.zipWith (\hp1 hm1 -> coef1 + coef2 * (hp1 + hm1)) (V.tail (V.tail hs)) hs > mus = mu_1 V.cons innerMus V.snoc mu_n > hs' <- V.mapM (\mu -> rvarT (Normal mu vh)) hs > let num1s = V.zipWith3 (\mu eta h -> logPdf (Normal mu eta) h) mus etas hs' > num2s = V.zipWith (\y h -> logPdf (Normal 0.0 (exp (0.5 * h))) y) ys hs' > nums = V.zipWith (+) num1s num2s > den1s = V.zipWith3 (\mu eta h -> logPdf (Normal mu eta) h) mus etas hs > den2s = V.zipWith (\y h -> logPdf (Normal 0.0 (exp (0.5 * h))) y) ys hs > dens = V.zipWith (+) den1s den2s > us <- V.replicate n <> rvarT StdUniform
>   let ls   = V.zipWith (\n d -> min 0.0 (n - d)) nums dens
>   return $V.zipWith4 (\u l h h' -> if log u < l then h' else h) us ls hs hs'  # Markov Chain Monte Carlo Now we can write down a single step for our Gibbs sampler, sampling from each marginal in turn. > singleStep :: Double -> V.Vector Double -> > (Double, Double, Double, Double, V.Vector Double) -> > StatsM (Double, Double, Double, Double, V.Vector Double) > singleStep vh y (mu, phi, tau2, h0, h) = do > lift$ tell [((mu, phi),tau2)]
>   hNew <- metropolis y mu phi tau2 h0 h vh
>   h0New <- sampleH0 iC0 iC0m0 hNew mu phi tau2
>   let bigX' = (S.col $S.vector$ replicate n 1.0)
>               |||
>               (S.col $S.vector$ V.toList $h0New V.cons V.init hNew) > bigX = bigX' asTypeOf (snd$ valAndType nT)
>   newParms <- sampleParms (S.vector $V.toList h) bigX (S.vector [mu0, phi0]) invBigV0 nu0 s02 > return ( (S.extract (fst newParms))!0 > , (S.extract (fst newParms))!1 > , snd newParms > , h0New > , hNew > )  # Testing Let’s create some test data. > mu', phi', tau2', tau' :: Double > mu' = -0.00645 > phi' = 0.99 > tau2' = 0.15^2 > tau' = sqrt tau2'  We need to create a statically typed matrix with one dimension the same size as the data so we tie the data size value to the required type. > nT :: Proxy 500 > nT = Proxy  > valAndType :: KnownNat n => Proxy n -> (Int, S.L n 2) > valAndType x = (fromIntegral$ natVal x, undefined)

> n :: Int
> n = fst $valAndType nT  Arbitrarily let us start the process at > h0 :: Double > h0 = 0.0  We define the process as a stream (aka co-recursively) using the Haskell recursive do construct. It is not necessary to do this but streams are a natural way to think of stochastic processes. > hs, vols, sds, ys :: V.Vector Double > hs = V.fromList$ take n $fst$ runState hsAux (pureMT 1)
>   where
>     hsAux = mdo { x0 <- sample (Normal (mu' + phi' * h0) tau')
>                 ; xs <- mapM (\x -> sample (Normal (mu' + phi' * x) tau')) (x0:xs)
>                 ; return xs
>                 }

> vols = V.map exp hs


We can plot the volatility (which we cannot observe directly).

And we can plot the log returns.

> sds = V.map sqrt vols

> ys = fst $runState ysAux (pureMT 2) > where > ysAux = V.mapM (\sd -> sample (Normal 0.0 sd)) sds  We start with a vague prior for $H_0$ > m0, c0 :: Double > m0 = 0.0 > c0 = 100.0  For convenience > iC0, iC0m0 :: Double > iC0 = recip c0 > iC0m0 = iC0 * m0  Rather than really sample from priors for $\mu, \phi$ and $\tau^2$ let us cheat and assume we sampled the simulated values! > mu0, phi0, tau20 :: Double > mu0 = -0.00645 > phi0 = 0.99 > tau20 = 0.15^2  But that we are still very uncertain about them > bigV0, invBigV0 :: S.Sq 2 > bigV0 = S.diag$ S.fromList [100.0, 100.0]
> invBigV0 = inv bigV0

> nu0, s02 :: Double
> nu0    = 10.0
> s02    = (nu0 - 2) / nu0 * tau20


Note that for the inverse gamma this gives

> expectationTau2, varianceTau2 :: Double
> expectationTau2 = (nu0 * s02 / 2) / ((nu0 / 2) - 1)
> varianceTau2 = (nu0 * s02 / 2)^2 / (((nu0 / 2) - 1)^2 * ((nu0 / 2) - 2))

ghci> expectationTau2
2.25e-2

ghci> varianceTau2
1.6874999999999998e-4


## Running the Markov Chain

Tuning parameter

> vh :: Double
> vh = 0.1


The burn-in and sample sizes may be too low for actual estimation but will suffice for a demonstration.

> bigM, bigM0 :: Int
> bigM0 = 2000
> bigM  = 2000

> multiStep :: StatsM (Double, Double, Double, Double, V.Vector Double)
> multiStep = iterateM_ (singleStep vh ys) (mu0, phi0, tau20, h0, vols)

> runMC :: [((Double, Double), Double)]
> runMC = take bigM $drop bigM0$
>         execWriter (evalStateT (sample multiStep) (pureMT 42))


And now we can look at the distributions of our estimates

# Bibliography

Doucet, Arnaud, and Adam M Johansen. 2011. “A Tutorial on Particle Filtering and Smoothing: Fifteen Years Later.” In Handbook of Nonlinear Filtering. Oxford, UK: Oxford University Press.

Fridman, Moshe, and Lawrence Harris. 1998. “A Maximum Likelihood Approach for Non-Gaussian Stochastic Volatility Models.” Journal of Business & Economic Statistics 16 (3): 284–91.

Geweke, John. 1994. “Bayesian Comparison of Econometric Models.”

Jacquier, Eric, Nicholas G. Polson, and Peter E. Rossi. 1994. “Bayesian Analysis of Stochastic Volatility Models.”

Kim, Sangjoon, Neil Shephard, and Siddhartha Chib. 1998. “Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models.” Review of Economic Studies 65 (3): 361–93. http://ideas.repec.org/a/bla/restud/v65y1998i3p361-93.html.

# A Type Safe Reverse or Some Hasochism

Conor McBride was not joking when he and his co-author entitled their paper about dependent typing in Haskell “Hasochism”: Lindley and McBride (2013).

In trying to resurrect the Haskell package yarr, it seemed that a dependently typed reverse function needed to be written. Writing such a function turns out to be far from straightforward. How GHC determines that a proof (program) discharges a proposition (type signature) is rather opaque and perhaps not surprisingly the error messages one gets if the proof is incorrect are far from easy to interpret.

I’d like to thank all the folk on StackOverflow whose answers and comments I have used freely below. Needless to say, any errors are entirely mine.

Here are two implementations, each starting from different axioms (NB: I have never seen type families referred to as axioms but it seems helpful to think about them in this way).

> {-# OPTIONS_GHC -Wall                     #-}
> {-# OPTIONS_GHC -fno-warn-type-defaults   #-}
> {-# OPTIONS_GHC -fno-warn-unused-do-bind  #-}
> {-# OPTIONS_GHC -fno-warn-missing-methods #-}
> {-# OPTIONS_GHC -fno-warn-orphans         #-}

> {-# LANGUAGE GADTs                        #-}
> {-# LANGUAGE KindSignatures               #-}
> {-# LANGUAGE DataKinds                    #-}
> {-# LANGUAGE TypeFamilies                 #-}
> {-# LANGUAGE UndecidableInstances         #-}
> {-# LANGUAGE ExplicitForAll               #-}
> {-# LANGUAGE TypeOperators                #-}
> {-# LANGUAGE ScopedTypeVariables          #-}


For both implementations, we need propositional equality: if a :~: b is inhabited by some terminating value, then the type a is the same as the type b. Further we need the normal form of an equality proof: Refl :: a :~: a and a function, gcastWith which allows us to use internal equality (:~:) to discharge a required proof of external equality (~). Readers familiar with topos theory, for example see Lambek and Scott (1988), will note that the notation is reversed.

> import Data.Type.Equality ( (:~:) (Refl), gcastWith )


For the second of the two approaches adumbrated we will need

> import Data.Proxy


The usual natural numbers:

> data Nat = Z | S Nat


We need some axioms:

1. A left unit and
2. Restricted commutativity.
> type family (n :: Nat) :+ (m :: Nat) :: Nat where
>     Z   :+ m = m
>     S n :+ m = n :+ S m


We need the usual singleton for Nat to tie types and terms together.

> data SNat :: Nat -> * where
>   SZero :: SNat Z
>   SSucc :: SNat n -> SNat (S n)


Now we can prove some lemmas.

First a lemma showing we can push :+ inside a successor, S.

> succ_plus_id :: SNat n1 -> SNat n2 -> (((S n1) :+ n2) :~: (S (n1 :+ n2)))
> succ_plus_id SZero _ = Refl
> succ_plus_id (SSucc n) m = gcastWith (succ_plus_id n (SSucc m)) Refl


This looks nothing like a standard mathematical proof and it’s hard to know what ghc is doing under the covers but presumably something like this:

• For SZero
1. S Z :+ n2 = Z :+ S n2 (by axiom 2) = S n2 (by axiom 1) and
2. S (Z + n2) = S n2 (by axiom 1)
3. So S Z :+ n2 = S (Z + n2)
• For SSucc
1. SSucc n :: SNat (S k) so n :: SNat k and m :: SNat i so SSucc m :: SNat (S i)
2. succ_plus id n (SSucc m) :: k ~ S p => S p :+ S i :~: S (p :+ S i) (by hypothesis)
3. k ~ S p => S p :+ S i :~: S (S p :+ i) (by axiom 2)
4. k :+ S i :~: S (k :+ i) (by substitution)
5. S k :+ i :~: S (k :+ i) (by axiom 2)

Second a lemma showing that Z is also the right unit.

> plus_id_r :: SNat n -> ((n :+ Z) :~: n)
> plus_id_r SZero = Refl
> plus_id_r (SSucc n) = gcastWith (plus_id_r n) (succ_plus_id n SZero)

• For SZero
1. Z :+ Z = Z (by axiom 1)
• For SSucc
1. SSucc n :: SNat (S k) so n :: SNat k
2. plus_id_r n :: k :+ Z :~: k (by hypothesis)
3. succ_plus_id n SZero :: S k :+ Z :~: S (k + Z) (by the succ_plus_id lemma)
4. succ_plus_id n SZero :: k :+ Z ~ k => S k :+ Z :~: S k (by substitution)
5. plus_id_r n :: k :+ Z :~: k (by equation 2)

Now we can defined vectors which have their lengths encoded in their type.

> infixr 4 :::

> data Vec a n where
>     Nil   :: Vec a Z
>     (:::) :: a -> Vec a n -> Vec a (S n)


We can prove a simple result using the lemma that Z is a right unit.

> elim0 :: SNat n -> (Vec a (n :+ Z) -> Vec a n)
> elim0 n x = gcastWith (plus_id_r n) x


Armed with this we can write an ${\mathcal{O}}(n)$ reverse function in which the length of the result is guaranteed to be the same as the length of the argument.

> size :: Vec a n -> SNat n
> size Nil         = SZero
> size (_ ::: xs)  = SSucc $size xs  > accrev :: Vec a n -> Vec a n > accrev x = elim0 (size x)$ go Nil x where
>     go :: Vec a m -> Vec a n -> Vec a (n :+ m)
>     go acc  Nil       = acc
>     go acc (x ::: xs) = go (x ::: acc) xs

> toList :: Vec a n -> [a]
> toList  Nil       = []
> toList (x ::: xs) = x : toList xs

> test0 :: [String]
> test0 = toList $accrev$ "a" ::: "b" ::: "c" ::: Nil

ghci> test0
["c","b","a"]


For an alternative approach, let us change the axioms slightly.

> type family (n1 :: Nat) + (n2 :: Nat) :: Nat where
>   Z + n2 = n2
>   (S n1) + n2 = S (n1 + n2)


Now the proof that Z is a right unit is more straightforward.

> plus_id_r1 :: SNat n -> ((n + Z) :~: n)
> plus_id_r1 SZero = Refl
> plus_id_r1 (SSucc n) = gcastWith (plus_id_r1 n) Refl


For the lemma showing we can push + inside a successor, S, we can use a Proxy.

> plus_succ_r1 :: SNat n1 -> Proxy n2 -> ((n1 + (S n2)) :~: (S (n1 + n2)))
> plus_succ_r1 SZero _ = Refl
> plus_succ_r1 (SSucc n1) proxy_n2 = gcastWith (plus_succ_r1 n1 proxy_n2) Refl


Now we can write our reverse function without having to calculate sizes.

> accrev1 :: Vec a n -> Vec a n
> accrev1 Nil = Nil
> accrev1 list = go SZero Nil list
>   where
>     go :: SNat n1 -> Vec a n1 -> Vec a n2 -> Vec a (n1 + n2)
>     go snat acc Nil = gcastWith (plus_id_r1 snat) acc
>     go snat acc (h ::: (t :: Vec a n3)) =
>       gcastWith (plus_succ_r1 snat (Proxy :: Proxy n3))
>                 (go (SSucc snat) (h ::: acc) t)

> test1 :: [String]
> test1 = toList $accrev1$ "a" ::: "b" ::: "c" ::: Nil

ghci> test0
["c","b","a"]


## Bibliography

Lambek, J., and P.J. Scott. 1988. Introduction to Higher-Order Categorical Logic. Cambridge Studies in Advanced Mathematics. Cambridge University Press. http://books.google.co.uk/books?id=6PY_emBeGjUC.

Lindley, Sam, and Conor McBride. 2013. “Hasochism: The Pleasure and Pain of Dependently Typed Haskell Programming.” In Proceedings of the 2013 ACM SIGPLAN Symposium on Haskell, 81–92. Haskell ’13. New York, NY, USA: ACM. doi:10.1145/2503778.2503786.

# Stopping Times

Let $S$ an $T$ be stopping times and let the filtration on which they are defined be right continuous. Then

1. $S \land T = \min(S, T)$,
2. $S \lor T = \max(S, T)$,
3. $S + T$ and
4. $\alpha T$

are stopping times where $\alpha > 1$.

For the first we have $\{S \land T \leq t\} = \{S \leq t\} \cup \{T \leq t\}$ and both the latter are in ${\mathcal{F}_t}$ by the definition of a stopping time.

Similarly for the second $\{S \lor T \leq t\} = \{S \leq t\} \cap \{T \leq t\}$.

For the fourth we have $\{\alpha T \leq t\} = \{T \leq \frac{t}{\alpha}\} \in {\mathcal{F}}_{t / \alpha} \subset {\mathcal{F}}_{t}$ since $\alpha > 1$.

The third is slightly trickier. For $\omega \in \Omega$, $S(\omega) +T(\omega) < t$ if and only if for some rational $q$, we have $S(\omega) + T(\omega) < q < t$. We can thus we can find $r \in \mathbb{Q}$ such that $S(\omega) < r < q - T(\omega)$. Writing $s \triangleq q - r \in \mathbb{Q}$ we also have $T(\omega) < q - r = s$. Thus we have $S(\omega) + T(\omega) < t$ if and only if there exist $r, s \in \mathbb{Q}$ and $r, s > 0$ such that $r + s < t$ and $S(\omega) < r$ and $T(\omega) < s$. In other words

$\displaystyle \{S +T < t\} = \bigcup_{r, s \in \mathbb{Q}^+} (\{S < r\} \cap \{T < s\})$

By right continuity (Protter 2004 Theorem 1) of the filtration, we know the terms on the right hand side are in ${\mathcal{F}}_r \subset {\mathcal{F}}_t$ and ${\mathcal{F}}_s \subset {\mathcal{F}}_t$ so that the whole right hand side is in ${\mathcal{F}}_t$. We thus know that the left hand side is in ${\mathcal{F}}_t$ and using right continuity again that therefore $S + T$ must be a stopping time.

# Bibliography

Protter, P.E. 2004. Stochastic Integration and Differential Equations: Version 2.1. Applications of Mathematics. Springer. http://books.google.co.uk/books?id=mJkFuqwr5xgC.

# Summary

An extended Kalman filter in Haskell using type level literals and automatic differentiation to provide some guarantees of correctness.

# Population Growth

Suppose we wish to model population growth of bees via the logistic equation

\displaystyle \begin{aligned} \dot{p} & = rp\Big(1 - \frac{p}{k}\Big) \end{aligned}

We assume the growth rate $r$ is unknown and drawn from a normal distribution ${\cal{N}}(\mu_r, \sigma_r^2)$ but the carrying capacity $k$ is known and we wish to estimate the growth rate by observing noisy values $y_i$ of the population at discrete times $t_0 = 0, t_1 = \Delta T, t_2 = 2\Delta T, \ldots$. Note that $p_t$ is entirely deterministic and its stochasticity is only as a result of the fact that the unknown parameter of the logistic equation is sampled from a normal distribution (we could for example be observing different colonies of bees and we know from the literature that bee populations obey the logistic equation and each colony will have different growth rates).

> {-# OPTIONS_GHC -Wall                     #-}
> {-# OPTIONS_GHC -fno-warn-type-defaults   #-}
> {-# OPTIONS_GHC -fno-warn-unused-do-bind  #-}
> {-# OPTIONS_GHC -fno-warn-missing-methods #-}
> {-# OPTIONS_GHC -fno-warn-orphans         #-}

> {-# LANGUAGE DataKinds                    #-}
> {-# LANGUAGE ScopedTypeVariables          #-}
> {-# LANGUAGE RankNTypes                   #-}
> {-# LANGUAGE BangPatterns                 #-}
> {-# LANGUAGE TypeOperators                #-}
> {-# LANGUAGE TypeFamilies                 #-}

> module FunWithKalman3 where

> import GHC.TypeLits
> import Numeric.LinearAlgebra.Static
> import Data.Maybe ( fromJust )

> import Numeric.AD

> import Data.Random.Source.PureMT
> import Data.Random

> import Control.Monad.State
> import qualified Control.Monad.Writer as W


## Logistic Equation

The logistic equation is a well known example of a dynamical system which has an analytic solution

$\displaystyle p = \frac{kp_0\exp rt}{k + p_0(\exp rt - 1)}$

> logit :: Floating a => a -> a -> a -> a
> logit p0 k x = k * p0 * (exp x) / (k + p0 * (exp x - 1))


We observe a noisy value of population at regular time intervals (where $\Delta T$ is the time interval)

\displaystyle \begin{aligned} p_i &= \frac{kp_0\exp r\Delta T i}{k + p_0(\exp r\Delta T i - 1)} \\ y_i &= p_i + \epsilon_i \end{aligned}

Using the semi-group property of our dynamical system, we can re-write this as

\displaystyle \begin{aligned} p_i &= \frac{kp_{i-1}\exp r\Delta T}{k + p_{i-1}(\exp r\Delta T - 1)} \\ y_i &= p_i + \epsilon_i \end{aligned}

To convince yourself that this re-formulation is correct, think of the population as starting at $p_0$; after 1 time step it has reached $p_1$ and and after two time steps it has reached $p_2$ and this ought to be the same as the point reached after 1 time step starting at $p_1$, for example

> oneStepFrom0, twoStepsFrom0, oneStepFrom1 :: Double
> oneStepFrom0  = logit 0.1 1.0 (1 * 0.1)
> twoStepsFrom0 = logit 0.1 1.0 (1 * 0.2)
> oneStepFrom1  = logit oneStepFrom0 1.0 (1 * 0.1)

ghci> twoStepsFrom0
0.11949463171139338

ghci> oneStepFrom1
0.1194946317113934


We would like to infer the growth rate not just be able to predict the population so we need to add another variable to our model.

\displaystyle \begin{aligned} r_i &= r_{i-1} \\ p_i &= \frac{kp_{i-1}\exp r_{i-1}\Delta T}{k + p_{i-1}(\exp r_{i-1}\Delta T - 1)} \\ y_i &= \begin{bmatrix}0 & 1\end{bmatrix}\begin{bmatrix}r_i \\ p_i\end{bmatrix} + \begin{bmatrix}0 \\ \epsilon_i\end{bmatrix} \end{aligned}

# Extended Kalman

This is almost in the form suitable for estimation using a Kalman filter but the dependency of the state on the previous state is non-linear. We can modify the Kalman filter to create the extended Kalman filter (EKF) by making a linear approximation.

Since the measurement update is trivially linear (even in this more general form), the measurement update step remains unchanged.

\displaystyle \begin{aligned} \boldsymbol{v}_i & \triangleq \boldsymbol{y}_i - \boldsymbol{g}(\hat{\boldsymbol{x}}^\flat_i) \\ \boldsymbol{S}_i & \triangleq \boldsymbol{G}_i \hat{\boldsymbol{\Sigma}}^\flat_i \boldsymbol{G}_i^\top + \boldsymbol{\Sigma}^{(y)}_i \\ \boldsymbol{K}_i & \triangleq \hat{\boldsymbol{\Sigma}}^\flat_i \boldsymbol{G}_i^\top\boldsymbol{S}^{-1}_i \\ \hat{\boldsymbol{x}}^i &\triangleq \hat{\boldsymbol{x}}^\flat_i + \boldsymbol{K}_i\boldsymbol{v}_i \\ \hat{\boldsymbol{\Sigma}}_i &\triangleq \hat{\boldsymbol{\Sigma}}^\flat_i - \boldsymbol{K}_i\boldsymbol{S}_i\boldsymbol{K}^\top_i \end{aligned}

By Taylor we have

$\displaystyle \boldsymbol{a}(\boldsymbol{x}) \approx \boldsymbol{a}(\boldsymbol{m}) + \boldsymbol{A}_{\boldsymbol{x}}(\boldsymbol{m})\delta\boldsymbol{x}$

where $\boldsymbol{A}_{\boldsymbol{x}}(\boldsymbol{m})$ is the Jacobian of $\boldsymbol{a}$ evaluated at $\boldsymbol{m}$ (for the exposition of the extended filter we take $\boldsymbol{a}$ to be vector valued hence the use of a bold font). We take $\delta\boldsymbol{x}$ to be normally distributed with mean of 0 and ignore any difficulties there may be with using Taylor with stochastic variables.

Applying this at $\boldsymbol{m} = \hat{\boldsymbol{x}}_{i-1}$ we have

$\displaystyle \boldsymbol{x}_i = \boldsymbol{a}(\hat{\boldsymbol{x}}_{i-1}) + \boldsymbol{A}_{\boldsymbol{x}}(\hat{\boldsymbol{x}}_{i-1})(\boldsymbol{x}_{i-1} - \hat{\boldsymbol{x}}_{i-1}) + \boldsymbol{\epsilon}_i$

Using the same reasoning as we did as for Kalman filters and writing $\boldsymbol{A}_{i-1}$ for $\boldsymbol{A}_{\boldsymbol{x}}(\hat{\boldsymbol{x}}_{i-1})$ we obtain

\displaystyle \begin{aligned} \hat{\boldsymbol{x}}^\flat_i &= \boldsymbol{a}(\hat{\boldsymbol{x}}_{i-1}) \\ \hat{\boldsymbol{\Sigma}}^\flat_i &= \boldsymbol{A}_{i-1} \hat{\boldsymbol{\Sigma}}_{i-1} \boldsymbol{A}_{i-1}^\top + \boldsymbol{\Sigma}^{(x)}_{i-1} \end{aligned}

Note that we pass in the Jacobian of the update function as a function itself in the case of the extended Kalman filter rather than the matrix representing the linear function as we do in the case of the classical Kalman filter.

> k, p0 :: Floating a => a
> k = 1.0
> p0 = 0.1 * k

> r, deltaT :: Floating a => a
> r = 10.0
> deltaT = 0.0005


Relating ad and hmatrix is somewhat unpleasant but this can probably be ameliorated by defining a suitable datatype.

> a :: R 2 -> R 2
> a rpPrev = rNew # pNew
>   where
>     (r, pPrev) = headTail rpPrev
>     rNew :: R 1
>     rNew = konst r
>
>     (p,  _) = headTail pPrev
>     pNew :: R 1
>     pNew = fromList $[logit p k (r * deltaT)]  > bigA :: R 2 -> Sq 2 > bigA rp = fromList$ concat $j [r, p] > where > (r, ps) = headTail rp > (p, _) = headTail ps > j = jacobian (\[r, p] -> [r, logit p k (r * deltaT)])  For some reason, hmatrix with static guarantees does not yet provide an inverse function for matrices. > inv :: (KnownNat n, (1 <=? n) ~ 'True) => Sq n -> Sq n > inv m = fromJust$ linSolve m eye


Here is the extended Kalman filter itself. The type signatures on the expressions inside the function are not necessary but did help the implementor discover a bug in the mathematical derivation and will hopefully help the reader.

> outer ::  forall m n . (KnownNat m, KnownNat n,
>                         (1 <=? n) ~ 'True, (1 <=? m) ~ 'True) =>
>           R n -> Sq n ->
>           L m n -> Sq m ->
>           (R n -> R n) -> (R n -> Sq n) -> Sq n ->
>           [R m] ->
>           [(R n, Sq n)]
> outer muPrior sigmaPrior bigH bigSigmaY
>       littleA bigABuilder bigSigmaX ys = result
>   where
>     result = scanl update (muPrior, sigmaPrior) ys
>
>     update :: (R n, Sq n) -> R m -> (R n, Sq n)
>     update (xHatFlat, bigSigmaHatFlat) y =
>       (xHatFlatNew, bigSigmaHatFlatNew)
>       where
>
>         v :: R m
>         v = y - (bigH #> xHatFlat)
>
>         bigS :: Sq m
>         bigS = bigH <> bigSigmaHatFlat <> (tr bigH) + bigSigmaY
>
>         bigK :: L n m
>         bigK = bigSigmaHatFlat <> (tr bigH) <> (inv bigS)
>
>         xHat :: R n
>         xHat = xHatFlat + bigK #> v
>
>         bigSigmaHat :: Sq n
>         bigSigmaHat = bigSigmaHatFlat - bigK <> bigS <> (tr bigK)
>
>         bigA :: Sq n
>         bigA = bigABuilder xHat
>
>         xHatFlatNew :: R n
>         xHatFlatNew = littleA xHat
>
>         bigSigmaHatFlatNew :: Sq n
>         bigSigmaHatFlatNew = bigA <> bigSigmaHat <> (tr bigA) + bigSigmaX


Now let us create some sample data.

> obsVariance :: Double
> obsVariance = 1e-2

> bigSigmaY :: Sq 1
> bigSigmaY = fromList [obsVariance]

> nObs :: Int
> nObs = 300

> singleSample :: Double -> RVarT (W.Writer [Double]) Double
> singleSample p0 = do
>   epsilon <- rvarT (Normal 0.0 obsVariance)
>   let p1 = logit p0 k (r * deltaT)
>   lift $W.tell [p1 + epsilon] > return p1  > streamSample :: RVarT (W.Writer [Double]) Double > streamSample = iterateM_ singleSample p0  > samples :: [Double] > samples = take nObs$ snd \$
>           W.runWriter (evalStateT (sample streamSample) (pureMT 3))


We created our data with a growth rate of

ghci> r
10.0


but let us pretend that we have read the literature on growth rates of bee colonies and we have some big doubts about growth rates but we are almost certain about the size of the colony at $t=0$.

> muPrior :: R 2
> muPrior = fromList [5.0, 0.1]
>
> sigmaPrior :: Sq 2
> sigmaPrior = fromList [ 1e2, 0.0
>                       , 0.0, 1e-10
>                       ]


We only observe the population and not the rate itself.

> bigH :: L 1 2
> bigH = fromList [0.0, 1.0]


Strictly speaking this should be 0 but this is close enough.

> bigSigmaX :: Sq 2
> bigSigmaX = fromList [ 1e-10, 0.0
>                      , 0.0, 1e-10
>                      ]


Now we can run our filter and watch it switch away from our prior belief as it accumulates more and more evidence.

> test :: [(R 2, Sq 2)]
> test = outer muPrior sigmaPrior bigH bigSigmaY
>        a bigA bigSigmaX (map (fromList . return) samples)