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A Stochastic approximation method for inference in probabilistic graphical models
Peter Carbonetto · Matthew King · Firas Hamze

Mon Dec 07 07:00 PM -- 11:59 PM (PST) @ None #None

We describe a new algorithmic framework for inference in probabilistic models, and apply it to inference for latent Dirichlet allocation. Our framework adopts the methodology of variational inference, but unlike existing variational methods such as mean field and expectation propagation it is not restricted to tractable classes of approximating distributions. Our approach can also be viewed as a sequential Monte Carlo (SMC) method, but unlike existing SMC methods there is no need to design the artificial sequence of distributions. Notably, our framework offers a principled means to exchange the variance of an importance sampling estimate for the bias incurred through variational approximation. Experiments on a challenging inference problem in population genetics demonstrate improvements in stability and accuracy over existing methods, and at a comparable cost.

Author Information

Peter Carbonetto (University of British Columbia)
Matthew King
Firas Hamze (D-Wave Systems)

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