Statistical Graph Signal Recovery Using Variational Bayes

The Variational Bayes Approach in Signal Processing

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...

Binary Graph-Signal Recovery from Noisy Samples

We study the problem of recovering a smooth graph signal from incomplete noisy measurements, using random sampling to choose from a subset of graph nodes. The signal recovery is formulated as a convex optimization problem. The optimality conditions form a system of linear equations which is solvable via Laplacian solvers. In particular, we use an incomplete Cholesky factorization conjugate grad...

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