- Bayesian Inference on the Brain John A.D. Aston and Adam M. Johansen Forecasting Indian Macroeconomic Variables Using Medium-Scale VAR Models Goodness C. Aye, Pami Dua, and Rangan Gupta Comparing Proportions: A Modern Solution to a Classical Problem Jose M. Bernardo Hamiltonian Monte Carlo for Hierarchical Models Michael Betancourt and Mark Girolami On Bayesian Spatio-Temporal Modeling of Oceanographic Climate Characteristics Madhuchhanda Bhattacharjee and Snigdhansu Chatterjee Sequential Bayesian Inference for Dynamic State Space Model Parameters Arnab Bhattacharya and Simon Wilson Bayesian Active Contours with Affine-Invariant Elastic Shape Prior Darshan Bryner and Anuj Srivastava Bayesian Semiparametric Longitudinal Data Modeling Using NI Densities Luis M. Castro, Victor H. Lachos, Diana M. Galvis, and Dipankar Bandyopadhyay Bayesian Factor Analysis Based on Concentration Yun Cao, Michael Evans, and Irwin Guttman Regional Fertility Data Analysis: A Small Area Bayesian Approach Eduardo A. Castro, Zhen Zhang, Arnab Bhattacharjee, Jose M. Martins, and Tapabrata Maiti In Search of Optimal Objective Priors for Model Selection and Estimation Jyotishka Datta and Jayanta K. Ghosh Bayesian Variable Selection for Predictively Optimal Regression Tanujit Dey and Ernest Fokoue Scalable Subspace Clustering with Application to Motion Segmentation Liangjing Ding and Adrian Barbu Bayesian Inference for Logistic Regression Models Using Sequential Posterior Simulation John Geweke, Garland Durham, and Huaxin Xu From Risk Analysis to Adversarial Risk Analysis David Rios Insua, Javier Cano, Michael Pellot, and Ricardo Ortega Symmetric Power Link with Ordinal Response Model Xun Jiang and Dipak K. Dey Elastic Prior Shape Models of 3D Objects for Bayesian Image Analysis Sebastian Kurtek and Qian Xie Multi-State Models for Disease Natural History Amy E. Laird, Rebecca A. Hubbard, and Lurdes Y.T. Inoue Priors on Hypergraphical Models via Simplicial Complexes Simon Lunagomez, Sayan Mukherjee, and Robert Wolpert A Bayesian Uncertainty Analysis for Nonignorable Nonresponse Balgobin Nandram and Namkyo Woo Stochastic Volatility and Realized Stochastic Volatility Models Yasuhiro Omori and Toshiaki Watanabe Monte Carlo Methods and Zero Variance Principle Theodore Papamarkou, Antonietta Mira, and Mark Girolami A Flexible Class of Reduced Rank Spatial Models for Large Non-Gaussian Dataset Rajib Paul, Casey M. Jelsema, and Kwok Wai Lau A Bayesian Reweighting Technique for Small Area Estimation Azizur Rahman and Satyanshu K. Upadhyay Empirical Bayes Methods for the Transformed Gaussian Random Field Model with Additive Measurement Errors Vivekananda Roy, Evangelos Evangelou, and Zhengyuan Zhu Mixture Kalman Filters and Beyond Saikat Saha, Gustaf Hendeby, and Fredrik Gustafsson Some Aspects of Bayesian Inference in Skewed Mixed Logistic Regression Models Cristiano C. Santos and Rosangela H. Loschi A Bayesian Analysis of the Solar Cycle Using Multiple Proxy Variables David C. Stenning, David A. van Dyk, Yaming Yu, and Vinay Kashyap Fuzzy Information, Likelihood, Bayes' Theorem, and Engineering Application Reinhard Viertl and Owat Sunanta Bayesian Parallel Computation for Intractable Likelihood Using Griddy-Gibbs Sampler Nuttanan Wichitaksorn and S.T. Boris Choy Index.
- (source: Nielsen Book Data)

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.

(source: Nielsen Book Data)