Package dev.nm.stat.random.rng.multivariate.mcmc.metropolis

Class MetropolisHastings

java.lang.Object

dev.nm.stat.random.rng.multivariate.mcmc.metropolis.AbstractMetropolis

dev.nm.stat.random.rng.multivariate.mcmc.metropolis.MetropolisHastings

All Implemented Interfaces:

RandomVectorGenerator, Seedable

public class MetropolisHastings
extends AbstractMetropolis

A generalization of the Metropolis algorithm, which allows asymmetric proposal functions.

See Also:

• • Wikipedia: Metropolis-Hastings
  • Liu, Jun S. "Ch.5.1, The Metropolis Algorithm (p.106-107 in pdf)," Monte Carlo Strategies in Scientific Computing. 2002.
  • Matlab's mhsample

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static interface	MetropolisHastings.ProposalDensityFunction	Defines the density of a proposal function, i.e.

Constructor Summary

Constructors

Constructor	Description
MetropolisHastings(RealScalarFunction logf, ProposalFunction proposalFunction, MetropolisHastings.ProposalDensityFunction proposalDensity, Vector initialState, RandomNumberGenerator rng)	Constructs a new instance with the given parameters.

Method Summary

Modifier and Type	Method	Description
protected boolean	isProposalAccepted(Vector x, Vector y)	Decides whether the given proposed state should be accepted, or whether the system should remain in it's current state.
protected Vector	nextProposedState(Vector currentState)	Proposes a next state for the system.

Methods inherited from class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.AbstractMetropolis

acceptanceRate, nextVector, seed

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Details

MetropolisHastings

public MetropolisHastings(RealScalarFunction logf,
 ProposalFunction proposalFunction,
 MetropolisHastings.ProposalDensityFunction proposalDensity,
 Vector initialState,
 RandomNumberGenerator rng)

Constructs a new instance with the given parameters.

Parameters:

logf - the log of the unnormalized pdf from which we wish to sample

proposalFunction - generates a proposal for the next state, given the current state

proposalDensity - the proposal density (see explanation in MetropolisHastings.ProposalDensityFunction)

initialState - the initial state of the algorithm

rng - a random number generator

Method Details

nextProposedState

protected Vector nextProposedState(Vector currentState)

Description copied from class: AbstractMetropolis

Proposes a next state for the system.

Specified by:

nextProposedState in class AbstractMetropolis

Parameters:

currentState - the current state of the system

Returns:

the proposed next state

isProposalAccepted

protected boolean isProposalAccepted(Vector x,
 Vector y)

Description copied from class: AbstractMetropolis

Decides whether the given proposed state should be accepted, or whether the system should remain in it's current state.

Specified by:

isProposalAccepted in class AbstractMetropolis

Parameters:

x - the current state of the system

y - the proposed next state of the system

Returns:

whether the system should accept the proposed next state