Bayesian Inference for Partially Identified Models

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The book explores the Bayesian approach to inference within partially identified models (PIMs), highlighting its effectiveness in such contexts. It provides a comprehensive overview of the statistical theory, properties, and applications related to PIMs, including those for misclassified data and instrumental variables. Drawing from extensive research, the author includes recent real data applications, illustrating the practical relevance and performance of Bayesian procedures in analyzing PIMs.