BAYESIAN MIXTURE MODELING BY MONTE CARLO SIMULATION
Radford M. Neal
Department of Computer Science
University of Toronto
June 1991
It is shown that Bayesian inference from data modeled by a
mixture distribution can feasibly be performed via Monte Carlo
simulation. This method exhibits the true Bayesian predictive
distribution, implicitly integrating over the entire underlying
parameter space. An infinite number of mixture components can
be accommodated without difficulty, using a prior distribution
for mixing proportions that selects a reasonable subset of
components to explain any finite training set. The need to decide
on a "correct" number of components is thereby avoided. The
feasibility of the method is shown empirically for a simple
classification task.