Patterns Of Scalable Bayesian Inference

Identifies unifying principles, patterns, and intuitions for scaling Bayesian inference. Reviews existing work on utilizing modern computing resources with both MCMC and variational approximation techniques. From this taxonomy of ideas, it characterizes the general principles that have proven successful for designing scalable inference procedures.

• | Author: Elaine Angelino, Matthew James Johnson, Ryan P. Adams
• | Publisher: Now Publishers Inc
• | Publication Date: Nov 17, 2016
• | Number of Pages: 148 pages
• | Language: English
• | Binding: Paperback
• | ISBN-10: 1680832182
• | ISBN-13: 9781680832181