Bayesian Statistics and New Generations BAYSM 2018, Warwick, UK, July 2-3 Selected Contributions /

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...

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Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Argiento, Raffaele (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Durante, Daniele (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Wade, Sara (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2019.
Έκδοση:1st ed. 2019.
Σειρά:Springer Proceedings in Mathematics & Statistics, 296
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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245 1 0 |a Bayesian Statistics and New Generations  |h [electronic resource] :  |b BAYSM 2018, Warwick, UK, July 2-3 Selected Contributions /  |c edited by Raffaele Argiento, Daniele Durante, Sara Wade. 
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505 0 |a 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. 
520 |a 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. 
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650 2 4 |a Statistics for Life Sciences, Medicine, Health Sciences.  |0 http://scigraph.springernature.com/things/product-market-codes/S17030 
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700 1 |a Durante, Daniele.  |e editor.  |4 edt  |4 http://id.loc.gov/vocabulary/relators/edt 
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