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Bayesian statistics and new generationsBAYSM 2018, Warwick, UK, July 2-3 : selected contributions /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bayesian statistics and new generationsedited by Raffaele Argiento, Daniele Durante, Sara Wade.
Reminder of title:	BAYSM 2018, Warwick, UK, July 2-3 : selected contributions /
remainder title:	BAYSM 2018
other author:	Argiento, Raffaele.
corporate name:
Published:	Cham :Springer International Publishing :2019.
Description:	xi, 184 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Bayesian statistical decision theoryTextbooks.
Online resource:	https://doi.org/10.1007/978-3-030-30611-3
ISBN:	9783030306113$q(electronic bk.)

Bayesian statistics and new generationsBAYSM 2018, Warwick, UK, July 2-3 : selected contributions /
Bayesian statistics and new generationsBAYSM 2018, Warwick, UK, July 2-3 : selected contributions /[electronic resource] :BAYSM 2018edited by Raffaele Argiento, Daniele Durante, Sara Wade. - Cham :Springer International Publishing :2019. - xi, 184 p. :ill., digital ;24 cm. - Springer proceedings in mathematics & statistics,v.2962194-1009 ;. - Springer proceedings in mathematics & statistics ;v.19..

Part I - Theory and Methods: A. Diana, J. Griffin, and E. Matechou, A Polya Tree Based Model for Unmarked Individuals in an Open Wildlife Population -- S. Haque and K. Mengersen, Bias Estimation and Correction Using Bootstrap Simulation of the Linking Process -- N. Laitonjam and N. Hurley, Non-parametric Overlapping Community Detection -- L. Fee Schneider, T. Staudt, and A. Munk, Posterior Consistency in the Binomial Model with Unknown Parameters: A Numerical Study -- C. Spire and D. Chakrabarty, Learning in the Absence of Training Data - a Galactic Application -- D. Tait and B. Worton, Multiplicative Latent Force Models -- PART II - Computational Statistics: N. Cunningham, J. E. Griffin, D. L. Wild, and A. Lee, particleMDI: A Julia Package for the Integrative Cluster Analysis of Multiple Datasets -- D. Hosszejni and G. Kastner, Approaches Toward the Bayesian Estimation of the Stochastic Volatility Model with Leverage -- B. Karimi and M. Lavielle, Efficient Metropolis-Hastings Sampling for Nonlinear Mixed Effects Models -- G. Kratzer, Reinhard Furrer, and Pittavino Marta. Comparison Between Suitable Priors for Additive Bayesian Networks -- I. Peneva and R. Savage, A Bayesian Nonparametric Model for Integrative Clustering of Omics Data -- I. Schwabe, Bayesian Inference of Interaction Effects in Item-Level Hierarchical Twin Data -- PART III - Applied Statistics: K. Brock, L. Billingham, C. Yap, and G. Middleton, A Phase II Clinical Trial Design for Associated Co-Primary Efficacy and Toxicity Outcomes with Baseline Covariates -- E. Lanzarone, E. Scalco, A. Mastropietro, S. Marzi, and G. Rizzo, A Conditional Autoregressive Model for estimating Slow and Fast Diffusion from Magnetic Resonance Images -- D. Rocha, M. Scotto, C. Pinto, J. Nuno Tavares, and S. Gouveia, Simulation Study of HIV Temporal Patterns Using Bayesian Methodology -- A. Shenvi, J. Smith, R. Walton, and S. Eldridge, Modelling with Non-Stratified Chain Event Graphs -- O. Stevenson and B. Brewer, Modelling Career Trajectories of Cricket Players Using Gaussian Processes -- F. Turner, R. Wilkinson, C. Buck, J. Jones, and L. Sime, Ice Cores and Emulation: Learning More About Past Ice Sheet Shapes.

This book presents a selection of peer-reviewed contributions to the fourth Bayesian Young Statisticians Meeting, BAYSM 2018, held at the University of Warwick on 2-3 July 2018. The meeting provided a valuable opportunity for young researchers, MSc students, PhD students, and postdocs interested in Bayesian statistics to connect with the broader Bayesian community. The proceedings offer cutting-edge papers on a wide range of topics in Bayesian statistics, identify important challenges and investigate promising methodological approaches, while also assessing current methods and stimulating applications. The book is intended for a broad audience of statisticians, and demonstrates how theoretical, methodological, and computational aspects are often combined in the Bayesian framework to successfully tackle complex problems.

ISBN: 9783030306113$q(electronic bk.)

Standard No.: 10.1007/978-3-030-30611-3doiSubjects--Topical Terms:

443102
Bayesian statistical decision theory
--Textbooks.

LC Class. No.: QA279.5 / .B39 2018

Dewey Class. No.: 519.542

Bayesian statistics and new generationsBAYSM 2018, Warwick, UK, July 2-3 : selected contributions /
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