There are many instances in genomics data analyses where measurements are made on a multivariate response. For example, alternative splicing can lead to multiple expressed isoforms from the same ...

The multinomial probit model has emerged as a useful framework for modeling nominal categorical data, but extending such models to multivariate measures presents computational challenges. Following a ...

This is an R / Python package for fitting multinomial probit (MNP) models, a form of generalized linear regression model for multi-class classification. It uses a fast approximation for the CDF of ...

Abstract: We have developed response-driven multinomial models, based on multivariate imaging features, to lateralize the epileptogenicity in temporal lobe epilepsy (TLE) patients. To this end, ...

Background: Chronic lung allograft dysfunction and its main phenotypes, bronchiolitis obliterans syndrome (BOS) and restrictive allograft syndrome (RAS), are major causes of mortality after lung ...

Departments of Mathematics, Montana State University-Bozeman, Bozeman, Montana 59717, and Department of Chemical and Fuels Engineering, University of Utah, 50 S. Central Campus Drive, Salt Lake City, ...

In this paper, we introduce a new flexible mixed model for multinomial discrete choice where the key individual- and alternative-specific parameters of interest are allowed to follow an ...

A Naive Bayes classifier is a probabilistic machine learning model that’s used for classification task. The crux of the classifier is based on the Bayes theorem. Multinomial Naive Bayes: This is ...

Different aspects of mathematical finance benefit from the use Hermite polynomials, and this is particularly the case where risk drivers have a Gaussian distribution. They support quick analytical ...

Abstract: Univariate Mixed Poisson distributions (MPDs) are commonly used to model data recorded from low flux objects or with short exposure times. They assume that the number of recorded events, ...

ABSTRACT: This contribution deals with a generative approach for the analysis of textual data. Instead of creating heuristic rules forthe representation of documents and word counts, we employ a ...