Bayesian Inference of a Multivariate Regression Model

Bayesian multivariate logistic regression.

Bayesian analyses of multivariate binary or categorical outcomes typically rely on probit or mixed effects logistic regression models that do not have a marginal logistic structure for the individual outcomes. In addition, difficulties arise when simple noninformative priors are chosen for the covariance parameters. Motivated by these problems, we propose a new type of multivariate logistic dis...

A Bayesian regression model for multivariate functional data

In this paper we present a model for the analysis of multivariate functional data with unequally spaced observation times that may differ among subjects. Our method is formulated as a Bayesian mixed-effects model in which the fixed part corresponds to the mean functions, and the random part corresponds to individual deviations from these mean functions. Covariates can be incorporated into both ...

Efficient Bayesian Multivariate Surface Regression

A Disease Outbreak Prediction Model Using Bayesian Inference: A Case of Influenza

Introduction: One major problem in analyzing epidemic data is the lack of data and high dependency among the available data, which is due to the fact that the epidemic process is not directly observable. Methods: One method for epidemic data analysis to estimate the desired epidemic parameters, such as disease transmission rate and recovery rate, is data ...

Bayesian Inference for the Multivariate Normal

Bayesian inference for the multivariate Normal is most simply instantiated using a Normal-Wishart prior over the mean and covariance. Predictive densities then correspond to multivariate T distributions, and the moments from the marginal densities are provided analytically or via Monte-Carlo sampling. We show how this textbook approach is applied to a simple two-dimensional example.