Variational-Inference

The beginning of my journey of Approximate Bayesian Inference

Step One: Start the Journey

Basic Concepts

1. Probabilisity Theory <>([pdf(more detailed)])
2. Convex Optimization

Resources

1. Machine Learning Course -- Stanford CS229, containing the basic concepts of ML.
2. UCI small dataset, it contains a large collection of standard datasets for testing learning algorithms.

Books for Reference

1. Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference [source code] [Note: Suggest reading through!]
2. Convex Optimization [Note: A little difficult to read, good for reference concerning convex optmization]

Step Two: Understand Basic Concepts of VI

1. From MLE (Maximum Likelihood Estimation) to EM (Expectation Maximization) [blog-cn] [cs229--more theoretical]

Step Three: Paper Reading

1. Advances in Variational Inference. [notes] [arkiv]
2. Bayesian Dark Knowledge. [notes] [arkiv]