Patterns of Scalable Bayesian Inference |
|
| Author:
| Angelino, Elaine
Johnson, Matthew James
Adams, Ryan P. |
| Series title: | Foundations and Trends® in Machine Learning Ser. |
| ISBN: | 978-1-68083-218-1 |
| Publication Date: | Nov 2016 |
| Publisher: | Now Publishers
|
| Book Format: | Paperback |
| List Price: | AUD $135.00 |
Book Description:
|
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.
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.