Cognitive Diagnosis Models (CDMs) are a special family of discrete latent variable models widely used in educational, psychological and social sciences. In many applications CDMs, certain hierarchical structures among the attributes assumed by researchers to characterize their dependence structure. Specifically, directed acyclic graph is specify constraints on allowable configurations attribute...

The research reported in this thesis has been partially carried out at CWI, the Dutch national research laboratory for mathematics and computer science, within the theme Data Mining and Knowledge Discovery, a subdivision of the research cluster Information Systems. To the memory of my mother A la memòria de ma mare

Abstract Acyclic phase-type (PH) distributions have been a popular tool in survival analysis, thanks to their natural interpretation terms of aging toward its inevitable absorption. In this article, we consider an extension the bivariate setting for modeling joint lifetimes. contrast previous models literature that were based on separate estimation marginal behavior and dependence structure thr...

the problem of estimating the parameter ?, when it is restricted to an interval of the form , in a class of discrete distributions, including binomial negative binomial discrete weibull and etc., is considered. we give necessary and sufficient conditions for which the bayes estimator of with respect to a two points boundary supported prior is minimax under squared log error loss function. for s...

A chain graph (CG) is a graph admitting both directed and undi-rected edges with (partially) directed cycles forbidden. It generalizes both the concept of undirected graph (UG) and the concept of directed acyclic graph (DAG). A chain graph can be used to describe eeciently the conditional independence structure of a multidimensional discrete probability distribution in the form of a graphoid, t...

The Bayesian network, with structure given by a directed acyclic graph (DAG), is a popular class of graphical models. However, learning Bayesian networks from discrete or categorical data is particularly challenging, due to the large parameter space and the difficulty in searching for a sparse structure. In this article, we develop a maximum penalized likelihood method to tackle this problem. I...