Résultats de recherche pour « Conditional distributions »

This chapter is about two recently published algorithms that can be used to sample from conditional distributions. We show how the efficiency of the algorithms can be improved when a sample is required from many conditional distributions. Using real-data examples from educational measurement, we show how the algorithms can be used to sample from intractable full-conditional distributions of the person and item parameters in an application of the Gibbs sampler.

In this paper we propose consistent integrated conditional moment tests for the validity of parametric conditional distribution models, based on the integrated squared difference between the empirical characteristic function of the actual data and the characteristic function implied by the model. To avoid numerical evaluation of the conditional characteristic function of the model distribution, a simulated integrated conditional moment test is proposed. As an empirical application we test the validity of a few common health economic count data models.

The notion of an infinite set of orthogonal conditional probability distributions describing quantum system states is introduced. The example of such orthogonal probability distributions is considered being associated with quantum oscillator states. The connection with separable and entangled probability distributions is discussed in the case of the probability distributions describing the oscillator energy states.

Abstract The majority of existing research aims to explain phenomena in various fields of application that rely on bivariate random variables [1]; [2]. Although these distributions have attracted some attention in the literature, little research exists on the bivariate compound distribution. Because of the computational difficulties in implementing such a distribution, research on it is limited. This study aims to use the saddle-point approximation method, which is more powerful than other methods. This article introduces conditional saddle-point approximations to the bivariate compound distribution in continuous and discrete settings. We discuss conditional approximations for cumulative distribution functions of bivariate compound distributions. Examples of continuous and discrete distributions from the bivariate compound truncated Poisson compound class are presented. Comparisons between saddle-point approximations and the exact calculations show the great accuracy of the saddle-point methods.