Bayesian Statistics in Action, 1st ed. 2017 BAYSM 2016, Florence, Italy, June 19-21 Springer Proceedings in Mathematics & Statistics Series, Vol. 194

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 decisionprocess

​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 biomedicalengineering 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, Montréal 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 Autónoma de México (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, sensitivi

Offers contributions presented by young Bayesian statisticians Presents a selection of innovative contributions Faithful community following the conference editions Includes supplementary material: sn.pub/extras