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We empirically evaluate a stochastic annealing strategy for Bayesianposterior optimization with variational inference. Variational inference is adeterministic approach to approximate posterior inference in Bayesian models inwhich a typically non-convex objective function is locally optimized over theparameters of the approximating distribution. We investigate an annealingmethod for optimizing this objective with the aim of finding a better localoptimal solution and compare with deterministic annealing methods and noannealing. We show that stochastic annealing can provide clear improvement onthe GMM and HMM, while performance on LDA tends to favor deterministicannealing methods.

Stochastic Annealing for Variational Inference