Bayesian Inference in the Social Sciences
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In addition to developing new methods and estimation techniques, his work features applications in a variety of disciplines, including micro- and macroeconomics, marketing, political science, transportation, and environmental engineering.
Almquist Carter T. Chan Cody Y.
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Skip to Main Content. First published: 26 September All rights reserved. About this book Presents new models, methods, and techniques and considers important real-world applications in political science, sociology, economics, marketing, and finance Emphasizing interdisciplinary coverage, Bayesian Inference in the Social Sciences builds upon the recent growth in Bayesian methodology and examines an array of topics in model formulation, estimation, and applications. The book is also useful for graduate-level courses in applied econometrics, statistics, mathematical modeling and simulation, numerical methods, computational analysis, and the social sciences.
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Tools Get online access For authors. Email or Customer ID. His work remains at the center of one of the main intellectual controversies of our time.
Bayes was the first to solve the problem of inverse probability. In its simplest form, given two events A and B with nonzero probability, the probability of A and B can be written as:.
Bayesian Inference in the Social Sciences | Wiley Online Books
For example, given that r heads out of n coin flips are observed, what is the probability of a head in a single coin flip? This allows one to work backwards, given the outcome or effect, to discover what is the probability of the cause. It is a direct consequence of the laws of probability. This relation is the foundation of Bayesian statistical inference, with the posterior distribution being the main component of statistical analysis. This provides a formal process of subjective learning from experience by showing how one can revise or update prior beliefs about parameters in the light of relevant sample evidence.
The role of judgment or outside information in statistical modeling is made explicit in the Bayesian approach.