Titlebook: Bayesian Statistics, New Generations New Approaches; BAYSM 2022, Montréal Alejandra Avalos-Pacheco,Roberta De Vito,Florian M Conference pro

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Mixing Times of a Gibbs Sampler for Probit Hierarchical Models,n to use a Gibbs sampler that alternates sampling from the full conditionals of the local and global parameters. Leveraging on recent advances in [.], we prove that the associated mixing times scale well as the number of groups grows, under warm start and random generating assumptions. The theoretical results are illustrated on simulated data.

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Expectation Propagation for the Smoothing Distribution in Dynamic Probit,apting a recent more general class of expectation propagation (.) algorithms, we derive an efficient . routine to perform inference for such a distribution. We show that the proposed approximation leads to accuracy gains over available approximate algorithms in a financial illustration.

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Bayesian Statistics, New Generations New ApproachesBAYSM 2022, Montréal

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A Variational Bayes Approach to Factor Analysis,models offers several benefits over the frequentist counterparts, including regularized estimates and inclusion of subjective prior information. However, implementation of Bayesian FA is routinely based on Markov Chain Monte Carlo (MCMC) techniques that are computationally expensive and often do not

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Speeding up the Zig-Zag Process,rocess, the Speed Up Zig-Zag (SUZZ) process, was later suggested in Vasdekis G. and Roberts G. O. (2023+) [.] as a way to explore the tails of the distribution faster, making it an ideal candidate for heavy tailed targets. In this article we will describe the SUZZ process, we will review the main th

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Extended Stochastic Block Model with Spatial Covariates for Weighted Brain Networks,s, i.e., common parameters for the generative process of the edges, which in turn represent connections among brain regions. Based on the neuroscience theory that neighboring regions are more likely to connect, the anatomical coordinates of each region can be leveraged, together with edges, to guide