# Tutorial "Variational Bayes and beyond: Bayesian inference for big data"

This tutorial took place at the Queensland University of Technology (QUT) on Monday, 2019 January 21, 11:00 AM -- 1:00 PM.
See this link for other tutorials.
**Instructor**:

Professor Tamara Broderick

Email:

## Materials and Description

**Title**: Variational Bayes and beyond: Bayesian inference for big data

**Abstract**:
Bayesian methods exhibit a number of desirable properties for modern
data analysis---including (1) coherent quantification of uncertainty,
(2) a modular modeling framework able to capture complex phenomena,
(3) the ability to incorporate prior information from an expert
source, and (4) interpretability. In practice, though, Bayesian
inference necessitates approximation of a high-dimensional integral,
and some traditional algorithms for this purpose can be slow---notably
at data scales of current interest. The tutorial will cover modern
tools for fast, approximate Bayesian inference at scale. One
increasingly popular framework is provided by "variational Bayes"
(VB), which formulates Bayesian inference as an optimization problem.
We will examine key benefits and pitfalls of using VB in practice,
with a focus on the widespread "mean-field variational Bayes" (MFVB)
subtype. We will highlight properties that anyone working with VB,
from the data analyst to the theoretician, should be aware of.
We will motivate our exploration
throughout with practical data analysis examples and point to a number
of open problems in the field.

In a seminar on the following day (Tuesday), we will cover recent data summarization techniques for
scalable Bayesian inference that come equipped with finite-data
theoretical guarantees on quality.

## Prerequisites

Basic familiarity with Bayesian data analysis and its goals. Be familiar with the following concepts: priors, likelihoods, posteriors, Bayes Theorem,
and conjugacy (for discrete and continuous distributions).