
Sequential Monte Carlo for Graphical Models
We propose a new framework for how to use sequential Monte Carlo (SMC) a...
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Graphical model inference: Sequential Monte Carlo meets deterministic approximations
Approximate inference in probabilistic graphical models (PGMs) can be gr...
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AutoEncoding Sequential Monte Carlo
We introduce AESMC: a method for using deep neural networks for simultan...
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Combining Generative and Discriminative Models for Hybrid Inference
A graphical model is a structured representation of the data generating ...
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Sequential Monte Carlo Inference of Mixed Membership Stochastic Blockmodels for Dynamic Social Networks
Many kinds of data can be represented as a network or graph. It is cruci...
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Learning the Structure of Deep Sparse Graphical Models
Deep belief networks are a powerful way to model complex probability dis...
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An invitation to sequential Monte Carlo samplers
Sequential Monte Carlo samplers provide consistent approximations of seq...
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Inference Networks for Sequential Monte Carlo in Graphical Models
We introduce a new approach for amortizing inference in directed graphical models by learning heuristic approximations to stochastic inverses, designed specifically for use as proposal distributions in sequential Monte Carlo methods. We describe a procedure for constructing and learning a structured neural network which represents an inverse factorization of the graphical model, resulting in a conditional density estimator that takes as input particular values of the observed random variables, and returns an approximation to the distribution of the latent variables. This recognition model can be learned offline, independent from any particular dataset, prior to performing inference. The output of these networks can be used as automaticallylearned highquality proposal distributions to accelerate sequential Monte Carlo across a diverse range of problem settings.
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