Variational Bayes With Intractable Likelihood

Variational Bayes with synthetic likelihood

Synthetic likelihood is an attractive approach to likelihood-free inference when an approximately Gaussian summary statistic for the data, informative for inference about the parameters, is available. The synthetic likelihood method derives an approximate likelihood function from a plug-in normal density estimate for the summary statistic, with plug-in mean and covariance matrix obtained by Mon...

Robust Inference with Variational Bayes

In Bayesian analysis, the posterior follows from the data and a choice of a prior and a likelihood. One hopes that the posterior is robust to reasonable variation in the choice of prior and likelihood, since this choice is made by the modeler and is necessarily somewhat subjective. For example, the process of prior elicitation may be prohibitively time-consuming, two practitioners may have irre...

Auto-Encoding Variational Bayes

How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributi...

Streaming Variational Bayes

Overview • Large, streaming data sets are increasingly the norm • Inference for Big Data has generally been non-Bayesian • Advantages of Bayes: complex models, coherent treatment of uncertainty, etc. We deliver: • SDA-Bayes, a framework for Streaming, Distributed, Asynchronous Bayesian inference • Experiments demonstrating streaming topic discovery with comparable predictive performance to non-...

Chapter 24 : Variational Bayes