Empirical Modeling in Economics: Specification and Evaluation Review

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(More customer reviews)This is probably the first and the last econometrics book I will have read just for fun! Although the title of the book sounds daunting, I found it very easy to read. If you are interested in intutions behind building econometric models, you should consider reading this book. A word of caution: if you want to find something that treats this subject rigorously, this may not be the book you want to buy. Just to have fun with econometrics is the general idea here.

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In these three essays, Professor Granger explains the process of constructing and evaluating an empirical model. Drawing on a wide range of cases and vignettes from economics, finance, politics and environment economics, as well as from art, literature, and the entertainment industry, Professor Granger combines rigor with intuition to provide a unique and entertaining insight into one of the most important subjects in modern economics. Chapter 1 deals with Specification. Chapter 2 considers Evaluation, and argues that insufficent evaluation is undertaken by economists, and that models should be evaluated in terms of the quality of their output. In Chapter 3, the question of how to evaluate forecasts is considered at several levels of increasing depth.

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Modeling and Reasoning with Bayesian Networks Review

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(More customer reviews)While pursuing my PhD at UCLA, I took Professor Darwiche's classes and had the privilege of using the pre-release version of this book. Before taking professor Darwiche's class, I had spent a good deal of time while working on my masters degree working on Bayesian networks. I found that much of the literature on Bayesian networks was inaccessible to someone new to the field. There simply was not a comprehensive resource that would explain Bayesian networks from the beginning in a through and clear manner. I say with confidence that this has now changed.
The book begins with the fundamentals of logic. It continues on to describe the properties of the Bayesian network graph such as independence relationships and d-separation as well as how the parameters of a Bayesian network work.
There are then in depth discussions of the various queries we are able to perform on Bayesian networks and the algorithms for accomplishing them. These include queries such as probability of evidence, most probable explanation and probabilistic inference. Techniques such as summing out, pearl's polytree algorithm and belief propagation are described elogently and clearly.
The book also contains information on the current state of the art research going on in the field. This book is a valuable resource for anyone new to or ingrained in the use of Bayesian Networks. A book of this scope and target was sorely needed and I for one am glad it has arrived. I would and have recommended this to any of my peers in the field.

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This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.

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