Merge-split proposals for conjugate "Chinese Restaurant Process" (CRP) mixture models using uniformly random allocation of items, as presented in Jain & Neal (2004), with additional functionality for the two parameter CRP prior.
simpleMergeSplit(
partition,
logPosteriorPredictiveDensity = function(i, subset) 0,
mass = 1,
discount = 0,
nUpdates = 1L,
selectionWeights = NULL
)A numeric vector of cluster labels representing the current partition.
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).
A specification of the mass (concentration) parameter in the CRP
prior. Must be greater than the -discount argument.
A numeric value on the interval [0,1) corresponding to the discount parameter in the two parameter CRP prior.
An integer giving the number of merge-split proposals before returning. This has the effect of thinning the Markov chain.
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.
An integer vector giving the updated partition encoded using cluster labels.
The acceptance rate
of the Metropolis-Hastings proposals, i.e. the number accepted proposals
divided by nUpdates.
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.