Titlebook: Bayesian Statistics in Action; BAYSM 2016, Florence Raffaele Argiento,Ettore Lanzarone,Alessandra Matt Conference proceedings 2017 Springer

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Chao Yin Wu,Hsiao Rong Huang,Mao Jiun Wangime, multiple time series, or observations with large and/or irregular temporal spacing. Computational tests show that the resulting Metropolis algorithm is capable of efficient inference for an electrical oscillator model.

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On the Study of Two Models for Integer-Valued High-Frequency Data likelihoods: the Skellam distribution - defined as the difference between two independent Poisson distributions - or a mixture of Geometric distributions. Posterior inference is obtained via adaptive Gibbs sampling algorithms. Comparisons of the models applied to high-frequency financial data is provided.

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Local Posterior Concentration Rate for Multilevel Sparse Sequenceson results. Global adaptive minimax results (for the estimation and posterior contraction problems) over sparsity classes follow from our local results if the sparsity level is of polynomial order. The results are illustrated by simulations.

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Likelihood Tempering in Dynamic Model Averaging. In this paper, we propose to suppress these issues by extending the Bayesian update by a sort of likelihood tempering, moderating the impact of observed data to inference. The method is compared to the generic dynamic model averaging and to an alternative solution via sequential quasi-Bayesian mixture modeling.

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Linear Inverse Problem with Range Prior on Correlations and Its Variational Bayes Inferenceunder the prior knowledge on range of their ratios. Since we aim at high dimensional problems, we use the Variational Bayes inference procedure to derive approximate inference of the model. The method is illustrated and compared on a simple example and on more realistic 6?h long release of mixture of 3 radionuclides.

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Randomization Inference and Bayesian Inference in Regression Discontinuity Designs: An Application td a variable equal to one for students who realize at least one University Credit (CFU), using both the Fisher-exact P-value approach and a model-based Bayesian approach. In both cases we account for the multivariate nature of the outcome by (a) proposing a multiple testing approach, and (b) defining estimands on the joint outcome.