Synopsis
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.
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About the Author
Albert Ando is Professor of Economics and Finance at the University of Pennsylvania.
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Paperback. Condition: New. Print on Demand. This book provides statisticians, researchers, and economists with a comprehensive guide to Bayesian probability, an analytic approach used to understand natural and social phenomena. Emerging from the author's seminal studies in the field, the book presents the ideas and principles underlying Bayesian theories, with a focus on their applications in multivariate problems where neither means nor uncertainties are known with certainty. The author highlights the advantages of Bayesian analysis over conventional frequency theory and explores the field's historical evolution, current development, and future prospects. This book is a valuable resource for practitioners seeking advanced statistical techniques to address complex problems in diverse fields, from econometrics to operations research and social science. This book is a reproduction of an important historical work, digitally reconstructed using state-of-the-art technology to preserve the original format. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in the book. print-on-demand item. Seller Inventory # 9781332975327_0
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