Bayesian Statistics in Action
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Bayesian Statistics in Action : BAYSM 2016, Florence, Italy, June 19-21

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Description

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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Product details

  • Hardback | 251 pages
  • 155 x 235 x 16mm | 5,207g
  • Cham, Switzerland
  • English
  • 1st ed. 2017
  • 44 Illustrations, color; 12 Illustrations, black and white; IX, 251 p. 56 illus., 44 illus. in color.
  • 3319540831
  • 9783319540832

Back cover copy

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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Table of contents

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 in a kinetic model of fluid and mass transfer during dialysis.- 14 A Bayesian nonparametric approach to ecological risk assessment.- 15 Approximate Bayesian Computation Methods in the identification of atmospheric contamination sources for DAPPLE experiment.- 16 Bayesian survival analysis to model plant resistance and tolerance to virus diseases.- 17 Randomization Inference and Bayesian Inference in Regression Discontinuity Design: An application to Italian University grants.- 18 Bayesian methods for microsimulation models.- 19 A Bayesian Model for Describing and Predicting the Stochastic Demand of Emergency Calls.- 20 Flexible Parallel Split-Merge MCMC for the HDP.- 21 Bayesian Inference for Continuous Time Animal Movement Based on Steps and Turns.- 22 Explaining the Lethality of Boko Haram's Terrorist Attacks in Nigeria, 2009-2014: A Hierarchical Bayesian Approach.- 23 Optimizing movement of cooperating pedestrians by exploiting floor-field model and Markov decision process
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About Raffaele Argiento

Raffaele Argiento is an assistant professor of statistics at the Department of Economic, Social, Mathematical and Statistical Sciences (ESOMAS) of the University of Turin, Italy. He obtained an M.Sc. degree in mathematics from Federico II University, Naples, Italy, in 2000 and a Ph.D. in statistics from Bocconi University, Milan, Italy, in 2007. He is affiliated to the "de Castro" Statistics initiative of the Collegio Carlo Alberto, Turin, Italy. He is a member of the board for the Ph.D. in statistics at Bocconi University. His research focuses on Bayesian parametric and nonparametric methods from both theoretical and applied viewpoints. He is the executive director of the Applied Bayesian Summer School (ABS) and a member of the BAYSM board. Ettore Lanzarone is a researcher at the Institute of Applied Mathematics and Information Technology ``E. Magenes'' (IMATI) at the National Research Council of Italy (CNR) in Milan, Italy. He obtained an M.Sc. degree in biomedical engineering and a Ph.D. in bioengineering from Politecnico di Milano, Italy, in 2004 and 2008, respectively. He is adjunct professor at the Department of Mathematics of Politecnico di Milano, Milan, Italy, and a collaborating member of the CIRRELT laboratory, Montreal and Quebec City, Canada. His research interests include prediction methods (Bayesian in particular), optimization and operations research, and bioengineering. He is cofounder of the BAYSM conferences and a member of the BAYSM board.



Isadora Antoniano-Villalobos is an assistant professor of statistics at the Department of Decision Sciences and a member of the board for the Ph.D. in statistics at Bocconi University, Milan, Italy. She obtained an M.Sc. degree in mathematics from the Universidad Nacional Autonoma de Mexico (UNAM), Mexico City, Mexico, in 2008 and a Ph.D. in statistics from the University of Kent, Canterbury, UK, in 2013. Her research focuses on nonparametric Bayesian models and methods, sensitivity analysis, and extreme value theory. She was chair-elect and chair of the junior section of the International Society for Bayesian Analysis (j-ISBA) in 2014 and 2015-2016.



Alessandra Mattei is an assistant professor of statistics at the Department of Statistics, Computer Science, Applications ``G. Parenti'' at the University of Florence, Italy, where she also obtained her M.A. in statistics and Ph.D. in applied statistics. In 2012 she was a research fellow at the Statistical and Applied Mathematical Sciences Institute (SAMSI), NC, USA. She has given short courses in Causal Inference. She is currently associate editor for the Journal of the Royal Statistical Society A. Her research interests include causal inference in experimental and observational studies, Bayesian inference, and inference with missing data problems.
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