KSLCore/ksl.utilities.random.mcmc

Package-level declarations

Types

class DMHMarkovChain(initialState: Int = 1, proposalMatrix: Array<DoubleArray>, alphaMatrix: Array<DoubleArray>, streamNumber: Int = 0, val streamProvider: RNStreamProviderIfc = KSLRandom.DefaultRNStreamProvider, name: String? = null) : DMarkovChain

Randomly generates the states of a discrete Markov Chain for a Metropolis-Hastings process. Assumes that the states are labeled 1, 2, 3, etc. The transition probabilities are supplied as an array of arrays. transMatrix0 holds the array of transition probabilities for transition to each state {p11, p12, p13, .., p1n} for state 1 transMatrix1 holds the array of transition probabilities for transition to each state {p21, p22, p23, .., p2n} for state 2 etc.

FunctionMVIfc

interface FunctionMVIfc

MetropolisHastings1D

class MetropolisHastings1D(var initialX: Double, targetFun: FunctionIfc, proposalFun: ProposalFunction1DIfc, streamNumber: Int = 0, val streamProvider: RNStreamProviderIfc = KSLRandom.DefaultRNStreamProvider) : Observable<Double> , RandomIfc

An implementation for a 1-Dimensional Metropolis Hasting process. The process is observable at each step

MetropolisHastingsMV

open class MetropolisHastingsMV(initialX: DoubleArray, val targetFun: FunctionMVIfc, val proposalFun: ProposalFunctionMVIfc, stream: RNStreamIfc = KSLRandom.nextRNStream(), batchStatistics: List<BatchStatistic> = createBatchStatistics(initialX.size)) : Observable<MetropolisHastingsMV> , MVSampleIfc, RNStreamChangeIfc, RNStreamControlIfc

An implementation for a multi-variable Metropolis Hasting process. The process is observable at each step

ProposalFunction1DIfc

interface ProposalFunction1DIfc

For use with MetropolisHastings1D. Represents the proposal function

ProposalFunctionMV

abstract class ProposalFunctionMV(val density: FunctionMVIfc) : ProposalFunctionMVIfc

ProposalFunctionMVIfc

interface ProposalFunctionMVIfc

For use with MetropolisHastingsMV. Represents the proposal function for the multivariate case.