Current trends in Bayesian methodology with applications /edited by Satyanshu Kumar Upadhyay, Umesh Singh, Dipak K. Dey, Appaia Loganathan.
Material type:
TextPublication details: Boca Raton, FL : CRC Pres, (c)2015.Description: 1 online resource (xxxix, 640 pages) : illustrationsContent type: - text
- computer
- online resource
- 9781482235128
- QA279 .C877 2015
- COPYRIGHT NOT covered - Click this link to request copyright permission: https://lib.ciu.edu/copyright-request-form
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G. Allen Fleece Library ONLINE | Non-fiction | QA279.5 (Browse shelf(Opens below)) | Link to resource | Available | ocn910237218 |
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Collecting Bayesian material scattered throughout the literature, Current Trends in Bayesian Methodology with Applications examines the latest methodological and applied aspects of Bayesian statistics. The book covers biostatistics, econometrics, reliability and risk analysis, spatial statistics, image analysis, shape analysis, Bayesian computation, clustering, uncertainty assessment, high-energy astrophysics, neural networking, fuzzy information, objective Bayesian methodologies, empirical Bayes methods, small area estimation, and many more topics.Each chapter is self-contained and focuses on.
Includes bibliographical references.
Front Cover; Contents; List of Figures; List of Tables; Preface; Foreword; List of Contributors; 1. Bayesian Inference on the Brain: Bayesian Solutions to Selected Problems in Neuroimaging; 2. Forecasting Indian Macroeconomic Variables Using Medium-Scale VAR Models; 3. Comparing Proportions: A Modern Solution to a Classical Problem; 4. Hamiltonian Monte Carlo for Hierarchical Models; 5. On Bayesian Spatio-Temporal Modeling of Oceanographic Climate Characteristics; 6. Sequential Bayesian Inference for Dynamic State Space Model Parameters
7. Bayesian Active Contours with Affine-Invariant Elastic Shape Prior8. Bayesian Semiparametric Longitudinal Data Modeling Using NI Densities; 9. Bayesian Factor Analysis Based on Concentration; 10. Regional Fertility Data Analysis: A Small Area Bayesian Approach; 11. In Search of Optimal Objective Priors for Model Selection and Estimation; 12. Bayesian Variable Selection for Predictively Optimal Regression; 13. Scalable Subspace Clustering with Application to Motion Segmentation; 14. Bayesian Inference for Logistic Regression Models Using Sequential Posterior Simulation
15. From Risk Analysis to Adversarial Risk Analysis16. Symmetric Power Link with Ordinal Response Model; 17. Elastic Prior Shape Models of 3D Objects for Bayesian Image Analysis; 18. Multi-State Models for Disease Natural History; 19. Priors on Hypergraphical Models via Simplicial Complexes; 20. A Bayesian Uncertainty Analysis for Nonignorable Nonresponse; 21. Stochastic Volatility and Realized Stochastic Volatility Models; 22. Monte Carlo Methods and Zero Variance Principle; 23. A Flexible Class of Reduced Rank Spatial Models for Large Non-Gaussian Dataset
24. A Bayesian Reweighting Technique for Small Area Estimation25. Empirical Bayes Methods for the Transformed Gaussian Random Field Model with Additive Measurement Errors; 26. Mixture Kalman Filters and Beyond; 27. Some Aspects of Bayesian Inference in Skewed Mixed Logistic Regression Models; 28. A Bayesian Analysis of the Solar Cycle Using Multiple Proxy Variables; 29. Fuzzy Information, Likelihood, Bayes' Theorem, and Engineering Application; 30. Bayesian Parallel Computation for Intractable Likelihood Using Griddy-Gibbs Sampler
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