mixVBEM: Estimate posterior distribution on mixture proportions of a...

In stephens999/ashr: Methods for Adaptive Shrinkage, using Empirical Bayes

Estimate posterior distribution on mixture proportions of a mixture model by a Variational Bayes EM algorithm

Description

Given the individual component likelihoods for a mixture model, estimates the posterior on the mixture proportions by an VBEM algorithm. Used by the ash main function; there is no need for a user to call this function separately, but it is exported for convenience.

Usage

mixVBEM(matrix_lik, prior, pi_init = NULL, control = list())

Arguments

matrix_lik	a n by k matrix with (j,k)th element equal to f_k(x_j).
prior	a k vector of the parameters of the Dirichlet prior on \pi. Recommended to be rep(1,k)
pi_init	the initial value of the posterior parameters. If not specified defaults to the prior parameters.
control	A list of control parameters for the SQUAREM algorithm, default value is set to be control.default=list(K = 1, method=3, square=TRUE, step.min0=1, step.max0=1, mstep=4, kr=1, objfn.inc=1,tol=1.e-07, maxiter=5000, trace=FALSE).

Details

Fits a k component mixture model

f(x|\pi) = \sum_k \pi_k f_k(x)

to independent and identically distributed data x_1,\dots,x_n. Estimates posterior on mixture proportions \pi by Variational Bayes, with a Dirichlet prior on \pi. Algorithm adapted from Bishop (2009), Pattern Recognition and Machine Learning, Chapter 10.

Value

A list, whose components include point estimates (pihat), the parameters of the fitted posterior on \pi (pipost), the bound on the log likelihood for each iteration (B) and a flag to indicate convergence (converged).

stephens999/ashr documentation built on May 16, 2024, 3:10 a.m.