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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.

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interface FunctionMVIfc
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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

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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

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For use with MetropolisHastings1D. Represents the proposal function

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