1,721,004 research outputs found
Objective Bayesian analysis for the multivariate skew-t model
Full text linkWe propose a novel Bayesian analysis of the p-variate skew-t model, providing a new parameterization, a set of non-informative priors and a sampler specifically designed to explore the posterior density of the model parameters. Extensions, such as the multivariate regression model with skewed errors and the stochastic frontiers model, are easily accommodated. A novelty introduced in the paper is given by the extension of the bivariate skew-normal model given in Liseo and Parisi (2013) to a more realistic p-variate skew-t model. We also introduce the R package mvst, which produces a posterior sample for the parameters of a multivariate skew-t model
Lezioni su: "Objective Bayesian Analysis: development of reference noninformative priors, by J.O. Berger"
No full text
Il lato oscuro dell’incertezza e i mille colori delle regole del caso: riflessioni e materiali per la divulgazione della probabilità
No full textThis paper is to be considered an almost random walk through the
wonderful world of probability. The main ideas and concepts, such as conditional probability, Bayes’ Theorem, expected value and the law of large numbers, will be discussed in an informal way with the help of historical and anecdotal examples
Bayes factors for Fieller's problem
No full textThis paper considers point null hypothesis testing when the sampling distribution belongs to a particular class, defined in Gleser & Hwang (1987). We discuss the drawbacks of frequentist and likelihood solutions and we show how proper Bayesian analysis encounters relatively similar difficulties. We explore the performance of several noninformative Bayesian approaches to testing, namely asymptotic approximations of Bayes factors and default Bayes factors. We argue that in a default Bayesian analysis of Fieller's problem the choice of the 'correct' prior distribution is crucial. Although standard and default Bayes factors based on Jeffreys' priors show, to a lesser extent, pathologies similar to those arising in a classical framework, default Bayes factors based on reference priors seem to correct the bias and provide sensible results in term of robustness and consistency
Approximate Bayesian conditional copulas
Full text linkCopula models are flexible tools to represent complex structures of dependence for multivariate random variables. According to Sklar's theorem, any multidimensional absolutely continuous distribution function can be uniquely represented as a copula, i.e. a joint cumulative distribution function on the unit hypercube with uniform marginals, which captures the dependence structure among the vector components. In real data applications, the interest of the analyses often lies on specific functionals of the dependence, which quantify aspects of it in a few numerical values. A broad literature exists on such functionals, however extensions to include covariates are still limited. This is mainly due to the lack of unbiased estimators of the conditional copula, especially when one does not have enough information to select the copula model. Several Bayesian methods to approximate the posterior distribution of functionals of the dependence varying according covariates are presented and compared; the main advantage of the investigated methods is that they use nonparametric models, avoiding the selection of the copula, which is usually a delicate aspect of copula modelling. These methods are compared in simulation studies and in two realistic applications, from civil engineering and astrophysics. (C) 2022 Elsevier B.V. All rights reserved
Alternative Approaches for Bayesian Binomial Mixtures: a Computational Comparative Analysis
No full textIntegrated likelihood Inference for the Shape Parameter of the Multivariate Skew-Normal Distribution
No full text- …
