Bayesian Statistics and New Generations

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

Raffaele Argiento is an Assistant Professor of Statistics at the Department of Economic, Social, Mathematical and Statistical Sciences (ESOMAS), University of Turin, Italy. He is member of the board for the Ph.D. in Modeling and Data Science at the same University and affiliated to the "de Castro" Statistics initiative hosted by the Collegio Carlo Alberto, Turin. 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.

Daniele Durante is an Assistant Professor of Statistics at the Department of Decision Sciences, Bocconi University, Italy, and a Research Affiliate at the Bocconi Institute for Data Science and Analytics (BIDSA). His research is characterized by its use of an interdisciplinary approach at the intersection of Bayesian methods, modern applications, and statistical learning to develop flexible and computationally tractable models for handling complex data. He was the chair of the Junior Section of the International Society for Bayesian Analysis (j-ISBA) in 2018.