// // Math.NET Numerics, part of the Math.NET Project // http://numerics.mathdotnet.com // http://github.com/mathnet/mathnet-numerics // // Copyright (c) 2009-2010 Math.NET // // Permission is hereby granted, free of charge, to any person // obtaining a copy of this software and associated documentation // files (the "Software"), to deal in the Software without // restriction, including without limitation the rights to use, // copy, modify, merge, publish, distribute, sublicense, and/or sell // copies of the Software, and to permit persons to whom the // Software is furnished to do so, subject to the following // conditions: // // The above copyright notice and this permission notice shall be // included in all copies or substantial portions of the Software. // // THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, // EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES // OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND // NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT // HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, // WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING // FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR // OTHER DEALINGS IN THE SOFTWARE. // 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; } } }