Current trends in bayesian methodology with applications

Table of Contents:

1. Bayesian Inference on the Brain
John A.D. Aston and Adam M. Johansen

2. Forecasting Indian Macroeconomic Variables Using Medium-Scale VAR Models
Goodness C. Aye, Pami Dua, and Rangan Gupta

3. Comparing Proportions: A Modern Solution to a Classical Problem
José M. Bernardo

4. Hamiltonian Monte Carlo for Hierarchical Models
Michael Betancourt and Mark Girolami

5. On Bayesian Spatio-Temporal Modeling of Oceanographic Climate Characteristics
Madhuchhanda Bhattacharjee and Snigdhansu Chatterjee

6. Sequential Bayesian Inference for Dynamic State Space Model Parameters
Arnab Bhattacharya and Simon Wilson

7. Bayesian Active Contours with Affine-Invariant Elastic Shape Prior
Darshan Bryner and Anuj Srivastava

8. Bayesian Semiparametric Longitudinal Data Modeling Using NI Densities
Luis M. Castro, Victor H. Lachos, Diana M. Galvis, and Dipankar Bandyopadhyay

9. Bayesian Factor Analysis Based on Concentration
Yun Cao, Michael Evans, and Irwin Guttman

10. Regional Fertility Data Analysis: A Small Area Bayesian Approach
Eduardo A. Castro, Zhen Zhang, Arnab Bhattacharjee, José M. Martins, and Tapabrata Maiti

11. In Search of Optimal Objective Priors for Model Selection and Estimation
Jyotishka Datta and Jayanta K. Ghosh

12. Bayesian Variable Selection for Predictively Optimal Regression
Tanujit Dey and Ernest Fokoué

13. Scalable Subspace Clustering with Application to Motion Segmentation
Liangjing Ding and Adrian Barbu

14. Bayesian Inference for Logistic Regression Models Using Sequential Posterior Simulation
John Geweke, Garland Durham, and Huaxin Xu

15. From Risk Analysis to Adversarial Risk Analysis
David Ríos Insua, Javier Cano, Michael Pellot, and Ricardo Ortega

16. Symmetric Power Link with Ordinal Response Model
Xun Jiang and Dipak K. Dey

17. Elastic Prior Shape Models of 3D Objects for Bayesian Image Analysis
Sebastian Kurtek and Qian Xie

18. Multi-State Models for Disease Natural History
Amy E. Laird, Rebecca A. Hubbard, and Lurdes Y.T. Inoue

19. Priors on Hypergraphical Models via Simplicial Complexes
Simón Lunagómez, Sayan Mukherjee, and Robert Wolpert

20. A Bayesian Uncertainty Analysis for Nonignorable Nonresponse
Balgobin Nandram and Namkyo Woo

21. Stochastic Volatility and Realized Stochastic Volatility Models
Yasuhiro Omori and Toshiaki Watanabe

22. Monte Carlo Methods and Zero Variance Principle
Theodore Papamarkou, Antonietta Mira, and Mark Girolami

23. A Flexible Class of Reduced Rank Spatial Models for Large Non-Gaussian Dataset
Rajib Paul, Casey M. Jelsema, and Kwok Wai Lau

24. A Bayesian Reweighting Technique for Small Area Estimation
Azizur Rahman and Satyanshu K. Upadhyay

25. Empirical Bayes Methods for the Transformed Gaussian Random Field Model with Additive Measurement Errors
Vivekananda Roy, Evangelos Evangelou, and Zhengyuan Zhu

26. Mixture Kalman Filters and Beyond
Saikat Saha, Gustaf Hendeby, and Fredrik Gustafsson

27. Some Aspects of Bayesian Inference in Skewed Mixed Logistic Regression Models
Cristiano C. Santos and Rosangela H. Loschi

28. A Bayesian Analysis of the Solar Cycle Using Multiple Proxy Variables
David C. Stenning, David A. van Dyk, Yaming Yu, and Vinay Kashyap

29. Fuzzy Information, Likelihood, Bayes’ Theorem, and Engineering Application
Reinhard Viertl and Owat Sunanta

30. Bayesian Parallel Computation for Intractable Likelihood Using Griddy-Gibbs Sampler
Nuttanan Wichitaksorn and S.T. Boris Choy

Index

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 a Bayesian methodology. It gives an overview of the area, presents theoretical insights, and emphasizes applications through motivating examples.

This book reflects the diversity of Bayesian analysis, from novel Bayesian methodology, such as nonignorable response and factor analysis, to state-of-the-art applications in economics, astrophysics, biomedicine, oceanography, and other areas. It guides readers in using Bayesian techniques for a range of statistical analyses.


(https://www.crcpress.com/Current-Trends-in-Bayesian-Methodology-with-Applications/Upadhyay-Singh-Dey-Loganathan/9781482235111)