variational inference
Post on 11-Feb-2017
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Part 2: Scalable Approximate Inference
Session 1:- Variational Inference
Session 2:- Sampling methods
Approximate and Scalable Inference for Complex Probabilistic Models in Recommender Systems
Introduction
Motivation: bayesian mixture model
Main idea
KL-Divergence
KL of the posterior
KL-Divergence
ELBO e KL-Divergence
Jensen inequality concave (log)
KL of the posterior
Evidence lower bound (ELBO)
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KL and ELBO
Choose family of variational distributions such that the expectations of log(q(z)) and log(p(x,z)) are computable
Mean-field
Optimizing a functional
Euler-lagrange equation
Mean-field
Structured variational inference
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