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using System;
using IStation.Numerics.Distributions;
namespace IStation.Numerics.Statistics.Mcmc
{
///
/// Metropolis sampling produces samples from distribution P by sampling from a proposal distribution Q
/// and accepting/rejecting based on the density of P. Metropolis sampling requires that the proposal
/// distribution Q is symmetric. All densities are required to be in log space.
///
/// The Metropolis sampler is a stateful sampler. It keeps track of where it currently is in the domain
/// of the distribution P.
///
/// The type of samples this sampler produces.
public class MetropolisSampler : McmcSampler
{
///
/// Evaluates the log density function of the sampling distribution.
///
private readonly DensityLn _pdfLnP;
///
/// A function which samples from a proposal distribution.
///
private readonly LocalProposalSampler _proposal;
///
/// The current location of the sampler.
///
private T _current;
///
/// The log density at the current location.
///
private double _currentDensityLn;
///
/// The number of burn iterations between two samples.
///
private int _burnInterval;
///
/// Constructs a new Metropolis sampler using the default random number generator.
///
/// The initial sample.
/// The log density of the distribution we want to sample from.
/// A method that samples from the symmetric proposal distribution.
/// The number of iterations in between returning samples.
/// When the number of burnInterval iteration is negative.
public MetropolisSampler(T x0, DensityLn pdfLnP, LocalProposalSampler proposal, int burnInterval = 0)
{
_current = x0;
_currentDensityLn = pdfLnP(x0);
_pdfLnP = pdfLnP;
_proposal = proposal;
BurnInterval = burnInterval;
Burn(BurnInterval);
}
///
/// Gets or sets the number of iterations in between returning samples.
///
/// When burn interval is negative.
public int BurnInterval
{
get => _burnInterval;
set
{
if (value < 0)
{
throw new ArgumentException("Value must not be negative (zero is ok).");
}
_burnInterval = value;
}
}
///
/// This method runs the sampler for a number of iterations without returning a sample
///
private void Burn(int n)
{
for (int i = 0; i < n; i++)
{
// Get a sample from the proposal.
T next = _proposal(_current);
// Evaluate the density at the next sample.
double p = _pdfLnP(next);
Samples++;
double acc = Math.Min(0.0, p - _currentDensityLn);
if (acc == 0.0)
{
_current = next;
_currentDensityLn = p;
Accepts++;
}
else if (Bernoulli.Sample(RandomSource, Math.Exp(acc)) == 1)
{
_current = next;
_currentDensityLn = p;
Accepts++;
}
}
}
///
/// Returns a sample from the distribution P.
///
public override T Sample()
{
Burn(BurnInterval + 1);
return _current;
}
}
}