Bayesian Inference for Partially Identified Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bayesian Inference for Partially Identified Models write by Paul Gustafson. This book was released on 2015-04-01. Bayesian Inference for Partially Identified Models available in PDF, EPUB and Kindle. Bayesian Inference for Partially Identified Models: Exploring the Limits of Limited Data 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. The book first describes how reparameterization can assist in computing posterior quantities and providing insight into the properties of Bayesian estimators. It next compares partial identification and model misspecification, discussing which is the lesser of the two evils. The author then works through PIM examples in depth, examining the ramifications of partial identification in terms of how inferences change and the extent to which they sharpen as more data accumulate. He also explains how to characterize the value of information obtained from data in a partially identified context and explores some recent applications of PIMs. In the final chapter, the author shares his thoughts on the past and present state of research on partial identification. This book helps readers understand how to use Bayesian methods for analyzing PIMs. Readers will recognize under what circumstances a posterior distribution on a target parameter will be usefully narrow versus uselessly wide.
Bayesian Inference for Partially Identified Models
Bayesian Inference for Partially Identified Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bayesian Inference for Partially Identified Models write by Paul Gustafson. This book was released on 2020-06-30. Bayesian Inference for Partially Identified Models available in PDF, EPUB and Kindle. 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 PIM
Bayesian inference with INLA
Bayesian inference with INLA - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bayesian inference with INLA write by Virgilio Gomez-Rubio. This book was released on 2020-02-20. Bayesian inference with INLA available in PDF, EPUB and Kindle. The integrated nested Laplace approximation (INLA) is a recent computational method that can fit Bayesian models in a fraction of the time required by typical Markov chain Monte Carlo (MCMC) methods. INLA focuses on marginal inference on the model parameters of latent Gaussian Markov random fields models and exploits conditional independence properties in the model for computational speed. Bayesian Inference with INLA provides a description of INLA and its associated R package for model fitting. This book describes the underlying methodology as well as how to fit a wide range of models with R. Topics covered include generalized linear mixed-effects models, multilevel models, spatial and spatio-temporal models, smoothing methods, survival analysis, imputation of missing values, and mixture models. Advanced features of the INLA package and how to extend the number of priors and latent models available in the package are discussed. All examples in the book are fully reproducible and datasets and R code are available from the book website. This book will be helpful to researchers from different areas with some background in Bayesian inference that want to apply the INLA method in their work. The examples cover topics on biostatistics, econometrics, education, environmental science, epidemiology, public health, and the social sciences.
Bayesian Inference for Partially Identified Models
Bayesian Inference for Partially Identified Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bayesian Inference for Partially Identified Models write by Paul Gustafson. This book was released on 2015-04-01. Bayesian Inference for Partially Identified Models available in PDF, EPUB and Kindle. Bayesian Inference for Partially Identified Models: Exploring the Limits of Limited Data 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. The book first describes how reparameterization can assist in computing posterior quantities and providing insight into the properties of Bayesian estimators. It next compares partial identification and model misspecification, discussing which is the lesser of the two evils. The author then works through PIM examples in depth, examining the ramifications of partial identification in terms of how inferences change and the extent to which they sharpen as more data accumulate. He also explains how to characterize the value of information obtained from data in a partially identified context and explores some recent applications of PIMs. In the final chapter, the author shares his thoughts on the past and present state of research on partial identification. This book helps readers understand how to use Bayesian methods for analyzing PIMs. Readers will recognize under what circumstances a posterior distribution on a target parameter will be usefully narrow versus uselessly wide.
Advances in Economics and Econometrics: Volume 2
Advances in Economics and Econometrics: Volume 2 - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Advances in Economics and Econometrics: Volume 2 write by Bo Honoré. This book was released on 2017-11-02. Advances in Economics and Econometrics: Volume 2 available in PDF, EPUB and Kindle. This is the second of two volumes containing papers and commentaries presented at the Eleventh World Congress of the Econometric Society, held in Montreal, Canada in August 2015. These papers provide state-of-the-art guides to the most important recent research in economics. The book includes surveys and interpretations of key developments in economics and econometrics, and discussion of future directions for a wide variety of topics, covering both theory and application. These volumes provide a unique, accessible survey of progress on the discipline, written by leading specialists in their fields. The second volume addresses topics such as big data, macroeconomics, financial markets, and partially identified models.
