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namespace IStation.Numerics.Statistics.Mcmc
{
using System;
///
/// Rejection sampling produces samples from distribution P by sampling from a proposal distribution Q
/// and accepting/rejecting based on the density of P and Q. The density of P and Q don't need to
/// to be normalized, but we do need that for each x, P(x) < Q(x).
///
/// The type of samples this sampler produces.
public class RejectionSampler : McmcSampler
{
///
/// Evaluates the density function of the sampling distribution.
///
private readonly Density _pdfP;
///
/// Evaluates the density function of the proposal distribution.
///
private readonly Density _pdfQ;
///
/// A function which samples from a proposal distribution.
///
private readonly GlobalProposalSampler _proposal;
///
/// Constructs a new rejection sampler using the default random number generator.
///
/// The density of the distribution we want to sample from.
/// The density of the proposal distribution.
/// A method that samples from the proposal distribution.
public RejectionSampler(Density pdfP, Density pdfQ, GlobalProposalSampler proposal)
{
_pdfP = pdfP;
_pdfQ = pdfQ;
_proposal = proposal;
}
///
/// Returns a sample from the distribution P.
///
/// When the algorithms detects that the proposal
/// distribution doesn't upper bound the target distribution.
public override T Sample()
{
while (true)
{
// Get a sample from the proposal.
T x = _proposal();
// Evaluate the density for proposal.
double q = _pdfQ(x);
// Evaluate the density for the target density.
double p = _pdfP(x);
// Sample a variable between 0.0 and proposal density.
double u = RandomSource.NextDouble() * q;
Samples++;
if (q < p)
{
throw new ArgumentException("The sampler\'s proposal distribution is not upper bounding the target density.");
}
if (u < p)
{
Accepts++;
return x;
}
}
}
}
}