this study compared the use of five different models to describe the growth from birth to 20 weeks of age of kids from both genders of nondescript goats. fifty nine (59) nondescript kids were weighed weekly at the university of maiduguri livestock teaching and research farm, nigeria and the live weights were modeled. biologically relevant variables were estimated for each kid from the logistic,...

Consider the problem of comparing parametric models M 1 ; : : : ; M k , when at least one of the models has an improper prior N i (i). Using the Bayes factor for comparing among these is not feasible due to arbitrary multiplicative constants in N (i). In this work we suggest adjusting the initial priors for each model, N i , by i (i) = Z N i (i jy)m (y)dy where m is a suitable predictive measur...

In this paper, we propose a method for the detection of irregularities in time series, based on linear prediction. We demonstrate how we can estimate the linear predictor by solving the Yule Walker equations, and how we can combine several predictors in a simple mixture model. In several tests, we compare our model to a Gaussian mixture and a hidden Markov model approach. We successfully apply ...

We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM’s). Learning is treated as a maximum likelihood problem; in particular, we present an Expectation-Maximization (EM) algorithm for adjusting the pa...

This article proposes a Bayesian infinite mixture model for the estimation of the conditional density of an ergodic time series. A nonparametric prior on the conditional density is described through the Dirichlet process. In the mixture model, a kernel is used leading to a dynamic nonlinear autoregressivemodel. This model can approximate any linear autoregressivemodel arbitrarily closely while ...

We study the prediction of latent variables in a finite mixture of linear structural equation models. The latent variables can be viewed as well-defined variables measured with error or as theoretical constructs that cannot be measured objectively, but for which proxies are observed. The finite mixture component may serve different purposes: it can denote an unobserved segmentation in subpopula...

High dimensional multiple testing has many important applications. Motivated by genome-wide association studies (GWAS), we consider the problem of mulitiple testing under high dimensional sparse linear model in order to identify the genetic markers associated with the trait of interest. The model is an extension of the normal mixture model under arbitrary dependence. We propose a multiple testi...

Majority of complex non-linear systems can be successfully modelled by a finite probabilistic mixture of linear models. The mixture model can be handled analytically, which is important for control of the system as well as for decision making. Quality of the model is a crucial requirement of all tasks of this type. The exact Bayesian methodology can not be used for estimation of this model, bec...