MIX-MC: Use Markov chain to do sampling for a mixture model. The mix-mc program is the specialization of xxx-mc to the task of sampling from the posterior distribution of the hyperparameters, parameters, and component indicators that are associated with a mixture model. If no training data is specified, the prior for hyperparameters is sampled from, in which case only the gibbs-hyper operation below is valid. The generic features of this program are described in xxx-mc.doc. However, at present, none of the pre-defined Markov chain operations can be used with mixture models, only the special operations described below. The state of the simulation has three parts: the hyperparameter values common to all mixture components, the indicators of which component is associated with each training case, and the parameter values for those components that are associated with some training case. Any parts that are not present when sampling begins are set as follows: hyperparameters to their means, parameters of components to their means given the hyperparameters, and indicators to a single component. The three parts of the state can be updated with the following application specific sampling operations: gibbs-hypers Does a Gibbs sampling scan over the hyperparameter values. gibbs-params Does a Gibbs sampling scan over the parameters for the mixture components that are currently associated with at least one training case. gibbs-indicators Does a Gibbs sampling scan over the indicators for which component is currently associated with each training case. This operation is not allowed for models with an infinite number of components. gibbs1-indicators Does a Gibbs sampling scan over the indicators for which component is currently associated with each training case, except that the component for a training case is not updated if this training case is currently associated with a singleton component - ie, if no other training case is associated with the same component as this one. This operation is possible for both finite and infinite models. Note that to produce an ergodic Markov chain, at least one of gibbs-indicators, met-indicators, and met1-indicators will need to be used as well. met-indicators [ N ] For each training case in turn, does N Metropolis-Hastings updates for the indicator that specifies which component is associated with that case. For each update, a new component is proposed, selected according to the predictive prior probability based on the current component frequencies and the concentration parameter. This component is then accepted or rejected based on the relative probability of the case with respect to the new and the old components. This operation is possible with both finite and infinite models. met1-indicators [ N ] This operation is like met-indicators, except that the new component proposed for a training case depends on whether or not the case is a singleton - ie, whether no other case has the same component. For singleton cases, the only components associated with training cases are proposed, with probabilities proportional to their predictive prior probabilities. For cases that are not singletons, only components that are not currently associated with any training case are proposed, with equal probabilities. The acceptance criterion is adjusted to make these proposals valid. This operation is possible with both finite and infinite models. Note that the gibbs-indicators, met-indicators, and met1-indicators operations may change the set of components that are associated with some training case. New components not previously associated with any training case have parameters drawn from the prior, given the current hyperparameters. Components are removed from the current list when they are no longer associated with any training case. Statistics regarding the met-indicators and met1-indicators operations can be accessed via the 'r', 'm', and 'D' quantities, as documented in mc-quantities.doc. The values of the 'm' and 'D' quantities are for the update of the component associated with one training case, selected at random each time. Copyright (c) 1997, 1998 by Radford M. Neal