The article proposes an improved method of auxiliary mixture sampling for count data, binomial data and multinomial data. In constrast to previously proposed samplers the method uses a limited number of latent variables per observation, independent of the intensity of the underlying Poisson process in the case of count data, or of the number of experiments in the case of binomial and multinomia...

The concept of a structural distribution function originates from linguistics. Let M denote the size of the vocabulary of an author and consider a text of this author that contains n words. Every choice of a word in the text from the vocabulary can be seen as the realization of a multinomial random vector. The whole text consists of a sequence of such choices X = (X (i) 1,M , . . . , X (i) M,M)...

The MM (minorization-maximization) principle is a versatile tool for constructing optimization algorithms. Every EM algorithm is an MM algorithm but not vice versa. This article derives MM algorithms for maximum likelihood estimation with discrete multivariate distributions such as the Dirichlet-multinomial and Connor-Mosimann distributions, the Neerchal-Morel distribution, the negative-multino...

Owning to the fact that ammunition can cause varying degrees of damage its target, this article presents a effectiveness calculation method hitting targets with based on Bayesian multinomial distribution solve problems complex processes, few trial times and difficult calculations probability in target-hitting tests high-tech ammunition, according index about occurrence different damage. Based c...

We present the first general formulas for central and non-central moments of multinomial distribution, using a combinatorial argument factorial previously obtained in Mosimann (1962). use to give explicit expressions all up order 8 4. These results expand significantly on those Newcomer (2008) et al. (2008), where were calculated

Multinomial choice and other nonlinear models are often used to estimate demand. We show how to nonparametrically identify the distribution of unobservables, such as random coefficients, that characterizes the heterogeneity among consumers in multinomial models. In particular, we provide general identification conditions for a class of nonlinear models and then verify these conditions using the...

Tests for the supremacy of a multinomial cell probability are developed. The tested null hypothesis states that a particular cell of interest is not more probable than all others. Rejection of this null leads to the conclusion that the cell of interest has a strictly greater probability than all other cells. The null hypothesis constrains the multinomial probability vector to a non-convex regio...

The logistic normal distribution has recently been adapted via the transformation of multivariate Gaussian variables to model the topical distribution of documents in the presence of correlations among topics. In this paper, we propose a probit normal alternative approach to modelling correlated topical structures. Our use of the probit model in the context of topic discovery is novel, as many ...

Motivation & Previous Work: A common objective in learning a model from data is to recover its network structure, while the model parameters are of minor interest. For example, we may wish to recover regulatory networks from high-throughput data sources. Regularization is essential when learning from finite data sets. It provides not only smoother estimates of the model parameters compared to m...