Bayesian inference via projections
Bayesian inference often poses difficult computational problems. Even when off-the-shelf Markov chain Monte Carlo (MCMC) methods are available to the problem at hand, mixing issues might compromise the quality of the results. We introduce a framework for situations where the model space can be naturally divided into two components: (i) a baseline black-box probability distribution for the observed variables and (ii) constraints enforced on functionals of this probability distribution. Inference is performed by sampling from the posterior implied by the first component, and finding projections on the space defined by the second component. We discuss the implications of this separation in terms of priors, model selection, and MCMC mixing in latent variable models. Case studies include probabilistic principal component analysis, models of marginal independence, and a interpretable class of structured ordinal probit models.
mcmc latent variable models optimization structured covariance matrices