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.

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