Bayesian Statistics and New Generations : BAYSM 2018, Warwick, UK, July 2-3 Selected Contributions. 1st ed. 2019
- 種類:
- 電子ブック
- 責任表示:
- edited by Raffaele Argiento, Daniele Durante, Sara Wade
- 出版情報:
- Cham : Springer International Publishing : Imprint: Springer, 2019
- 著者名:
- シリーズ名:
- Springer Proceedings in Mathematics & Statistics ; 296
- ISBN:
- 9783030306113 [3030306119]
- 注記:
- 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 Eff
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. - ローカル注記:
- 岐阜大学構成員専用E-BOOKS (Gifu University members only)
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