Bayesian Inference for Partially Identified Models

This book shows how the Bayesian approach to inference is applicable to partially identified models (PIMs) and examines the performance of Bayesian procedures in partially identified contexts. Drawing on his many years of research in this area, the author presents a thorough overview of the statistical theory, properties, and applications of PIMs. He covers a range of PIMs, including models for misclassified data and models involving instrumental variables. He also includes real data applications of PIMs that have recently appeared in the literature.