Huillet, T.: Unordered and ordered sample from Dirichlet distribution. Ann. Inst. Stat. Math. 57(3), 597- 616 (2005)Huillet, T.: Unordered and ordered sample from Dirichlet distribution. Ann. Inst. Stat. Math. 57 (3), 597–616 (2005)...
Extending such estimation to distributions such as Dirichlet-Multinomial or multivariate Poisson where the covariance matrix is parameterized through the mean is very challenging. Hypothesis tests for covariance matrices have primarily been developed by studying the asymptotic properties of traditional test ...
Beta distributionDirichlet distributionMajorizationSchur convex functionsPrimary 97K80Secondary 62F03The introduction of shape parameters into statistical distributions provided flexible models that produced better fit to experimental data. The Weibull and gamma families are prime examples wherein shape ...
Topic models have emerged as fundamental tools in unsupervised machine learning. Most modern topic models take a probabilistic view and are derived from Latent Dirichlet allocation (LDA). While efficient algorithms to estimate topic-word distribution from the observed documents for LDA are known, it ...
(Pre-Talk) 27:44 The question of q, a look at the interplay of number theory and ergodic theory i 57:37 The value distribution of the Hurwitz zeta function with an irrational shift 51:58 Theta-finite pro-Hermitian vector bundles from loop groups elements 51:02 Torsion points and ...
Extensions of belief functions and possibility distributions by using the imprecise Dirichlet model Fuzzy Sets and Systems, 154 (3) (2005), pp. 413-431 View PDFView articleView in ScopusGoogle Scholar [37] P. Walley Belief function representations of statistical evidence The Annals of Statistics,...
Non Gaussian distributions: we have also implemented a version of the hierarchical Naïve Bayes approach for discrete variables, relying on multinomial and Dirichlet probability distributions [34, 35]. This extension allows managing arbitrary data distributions after proper discretization.Conclusion...
In particular, we specify a normal-gamma prior for μj [69], an inverse-Wishart prior for Σ, and a Dirichlet distribution for πr (details in the Additional file 1: Supplementary notes). The hyper-parameter β in the Potts model, however, is difficult to infer algorithmically because of...
Benavoli, A., Corani, G., Mangili, F., Zaffalon, M., Ruggeri, F.: A Bayesian Wilcoxon signed-rank test based on the Dirichlet process. In: 30th International conference on machine learning, pp. 1026–1034 (2014) Benavoli, A., Corani, G., Demšar, J., Zaffalon, M.: Time for...
In this paper, a normal mixture sample selection model based on Bayesian MCMC and Dirichlet prior is developed in the parametric framework to relax the normal coupling assumption in the traditional sample selection model. After detailed theoretical derivation, a simulation example is conducted to justif...