Bayesian Statistics in Action : BAYSM 2016, Florence, Italy, June 19-21. 1st ed. 2017

種類:

電子ブック

責任表示:

edited by Raffaele Argiento, Ettore Lanzarone, Isadora Antoniano Villalobos, Alessandra Mattei

出版情報:

Cham : Springer International Publishing : Imprint: Springer, 2017

著者名:

• Argiento, Raffaele.
• Lanzarone, Ettore.
• Antoniano Villalobos, Isadora.
• Mattei, Alessandra.
• SpringerLink (Online service)

シリーズ名:

Springer Proceedings in Mathematics & Statistics ; 194

ISBN:

9783319540849 [331954084X]    

注記:

Part I THEORY AND METHODS -- 1 Sequential Monte Carlo methods in random intercept models for longitudinal data -- 2 On the truncation error of a superposed gamma process -- 3 On the study of two models for integer valued high-frequency data -- 4 Identification and Estimation of Principal Causal Effects in Randomized Experiments with Treatment Switching -- 5 A Bayesian Joint Dispersion Model with Flexible Links -- 6 Local posterior concentration rate for multilevel sparse sequences -- 7 Likelihood Tempering in Dynamic Model Averaging -- 8 Localization in High-Dimensional Monte Carlo Filtering -- 9 Linear inverse problem with range prior on correlations and its Variational Bayes. Part II APPLICATIONS AND CASE STUDIES -- 10 Bayesian hierarchical model for assessment of climate model biases -- 11 An application of Bayesian seemingly unrelated regression models with flexible tails -- 12 Bayesian Inference of Stochastic Pursuit Models from Basketball Tracking Data -- 13 Identification of patient-specific parameters
This book is a selection of peer-reviewed contributions presented at the third Bayesian Young Statisticians Meeting, BAYSM 2016, Florence, Italy, June 19-21. The meeting provided a unique opportunity for young researchers, M.S. students, Ph.D. students, and postdocs dealing with Bayesian statistics to connect with the Bayesian community at large, to exchange ideas, and to network with others working in the same field. The contributions develop and apply Bayesian methods in a variety of fields, ranging from the traditional (e.g., biostatistics and reliability) to the most innovative ones (e.g., big data and networks).

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