Extendable priors for multivariate normal models that keep analysis simple and flexible. This work presents an approach to broaden the Normal-Wishart family of priors so you can assign diagonal and other prior variances more freely to the parameters of a multivariate normal data-generating process. The authors show how to preserve analytical convenience while gaining much greater control over prior beliefs.
- Learn how to form extended conjugate distributions that stay the same functional form when updated with data.
- See how marginal distributions for the mean vector and the covariance-like parameter can be derived easily.
- Explore how these extensions support straightforward preposterior analysis and decision making.
- Understand the role of Bellman-type extensions and how they influence practical Bayesian modeling.
Ideal for readers of Bayesian statistics and multivariate analysis who want more flexible priors without sacrificing tractability.
Forgotten Books publishes hundreds of thousands of rare and classic books.
This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.
This text has been digitally restored from a historical edition. Some aesthetic errors may persist, however it has been deemed that any such will not detract from the work's significant historical value.
The digital edition of all books may be viewed on our website before purchase.Our books are made to order. After purchase your chosen title will be specially printed. Due to the individual nature of the process, please allow 3-4 business days for printing before we can dispatch.