psmMergeSplit: Merge-Split Sampling for a Partition Based on Sequential...

In sams: Merge-Split Samplers for Conjugate Bayesian Nonparametric Models

Merge-Split Sampling for a Partition Based on Sequential Allocation Informed by Pairwise Similarities

Description

Merge-split proposals for conjugate "Chinese Restaurant Process" (CRP) mixture models using sequentially-allocated elements. Allocation is performed with weights derived from a previously-calculated pairwise similarity matrix, and optionally complemented with "restricted Gibbs" scans as discussed in Jain & Neal (2004).

Usage

psmMergeSplit(
  partition,
  psm,
  logPosteriorPredictiveDensity = function(i, subset) 0,
  t = 1,
  mass = 1,
  discount = 0,
  nUpdates = 1L,
  selectionWeights = NULL
)

Arguments

partition	A numeric vector of cluster labels representing the current partition.
psm	A matrix of previously-calculated pairwise similarity probabilities for each pair of data indices.
logPosteriorPredictiveDensity	A function taking an index i (as a numeric vector of length one) and a subset of integers subset, and returning the natural logarithm of p( y_i | y_subset ), i.e., that item's contribution to the log integrated likelihood given a subset of the other items. The default value "turns off" the likelihood, resulting in prior simulation (rather than posterior simulation).
t	A non-negative integer indicating the number of restricted Gibbs scans to perform for each merge/split proposal.
mass	A specification of the mass (concentration) parameter in the CRP prior. Must be greater than the -discount argument.
discount	A numeric value on the interval [0,1) corresponding to the discount parameter in the two-parameter CRP prior.
nUpdates	An integer giving the number of merge-split proposals before returning. This has the effect of thinning the Markov chain.
selectionWeights	A matrix or data frame whose first two columns are the unique pairs of data indices, along with a column of weights representing how likely each pair is to be selected at the beginning of each merge-split update.

Value

partition

A numeric vector giving the updated partition encoded using cluster labels.

accept

The acceptance rate of the Metropolis-Hastings proposals, i.e. the number of accepted proposals divided by nUpdates.

References

Jain, S., & Neal, R. M. (2004). A split-merge Markov chain Monte Carlo procedure for the Dirichlet process mixture model. Journal of computational and Graphical Statistics, 13(1), 158-182.

Examples

# Neal (2000) model and data
nealData <- c(-1.48, -1.40, -1.16, -1.08, -1.02, 0.14, 0.51, 0.53, 0.78)
mkLogPosteriorPredictiveDensity <- function(data = nealData,
                                            sigma2 = 0.1^2,
                                            mu0 = 0,
                                            sigma02 = 1) {
  function(i, subset) {
    posteriorVariance <- 1 / ( 1/sigma02 + length(subset)/sigma2 )
    posteriorMean <- posteriorVariance * ( mu0/sigma02 + sum(data[subset])/sigma2 )
    posteriorPredictiveSD <- sqrt(posteriorVariance + sigma2)
    dnorm(data[i], posteriorMean, posteriorPredictiveSD, log=TRUE)
  }
}

logPostPredict <- mkLogPosteriorPredictiveDensity()

nSamples <- 1100L
nBurn <- 100
partitions <- matrix(0, nrow=nSamples, ncol=length(nealData))

# initial draws to inform similarity matrix
for ( i in 2:nBurn ) {
  partitions[i,] <- nealAlgorithm3(partitions[i-1,],
                                   logPostPredict,
                                   mass = 1,
                                   nUpdates = 1)
}

# Generate pairwise similarity matrix from initial draws
psm.mat <- psm(partitions[1:nBurn,])

accept <- 0
for ( i in (nBurn+1):nSamples ) {
  ms <- psmMergeSplit(partitions[i-1,],
                      psm.mat,
                      logPostPredict,
                      t = 1,
                      mass = 1.0,
                      nUpdates = 1)
  partitions[i,] <- ms$partition
  accept <- accept + ms$accept
}

accept / (nSamples - nBurn) # post burn-in M-H acceptance rate
nSubsets <- apply(partitions, 1, function(x) length(unique(x)))
mean(nSubsets)
sum(acf(nSubsets)$acf)-1   # Autocorrelation time

entropy <- apply(partitions, 1, partitionEntropy)
plot.ts(entropy)

sams documentation built on April 20, 2022, 1:06 a.m.