Parametric and nonparametric methods have been developed for purposes of predicting phenotypes. These methods are based on retrospective analyses of empirical data consisting of genotypic and phenotypic scores. Recent reports have indicated that parametric methods are unable to predict phenotypes of traits with known epistatic genetic architectures. Herein, we review parametric methods includin...

Parametric and nonparametric methods have been developed for purposes of predicting phenotypes. These methods are based on retrospective analyses of empirical data consisting of genotypic and phenotypic scores. Recent reports have indicated that parametric methods are unable to predict phenotypes of traits with known epistatic genetic architectures. Herein, we review parametric methods includin...

We review gene mapping, or inference for quantitative trait loci, in the context of recent research in semi-parametric and non-parametric inference for mixture models. Gene mapping studies the relationship between a phenotypic trait and inherited genotype. Semi-parametric gene mapping using the exponential tilt covers most standard exponential families and improves estimation of genetic effects...

AIM For the purpose of cost modeling, the semi-parametric single-index two-part model was utilized in the paper. Furthermore, as functional gastrointestinal diseases which are well-known as common causes of illness among the society people in terms of both the number of patients and prevalence in a specific time interval, this research estimated the average cost of functional gastrointestinal d...

In this article we develop a nonparametric Bayesian approach to prediction for the M/G/1 queue, focusing on the imbedded semiMarkov process of the queue at the departure times. Our approach is motivated by queues with a large number of data points and highfrequency systems, where times consuming MCMC/ABC algorithms might be infeasible and a nonparametric approach is desirable to avoid parametri...

In a recent paper, Friedman, Geiger, and Goldszmidt [8] introduced a classifier based on Bayesian networks, called Tree Augmented Naive Bayes (TAN), that outperforms naive Bayes and performs competitively with C4.5 and other state-of-the-art methods. This classifier has several advantages including robustness and polynomial computational complexity. One limitation of the TAN classifier is that ...

Unsupervised clustering of utterances can be useful for the modeling of dialogue acts for dialogue applications. Previously, the Chinese restaurant process (CRP), a non-parametric Bayesian method, has been introduced and has shown promising results for the clustering of utterances in dialogue. This paper newly introduces the infinite HMM, which is also a nonparametric Bayesian method, and verif...

OF THE DISSERTATION Networks of Mixture Blocks for Non Parametric Bayesian Models with Applications By Ian Porteous Doctor of Philosophy in Information and Computer Science University of California, Irvine, 2010 Professor Max Welling, Chair This study brings together Bayesian networks, topic models, hierarchical Bayes modeling and nonparametric Bayesian methods to build a framework for efficien...

The Minimum Description Length (MDL) approach to parametric model selection chooses a model that provides the shortest codelength for data, while the Bayesian approach selects the model that yields the highest likelihood for the data. In this article I describe how the Bayesian approach yields essentially the same model selection criterion as MDL provided one chooses a Jeffreys prior for the pa...

This study extends the parametric estimation of the structural misclassification model (Arrieta, 2008) to analyze over-treatment of medical procedures to a semi-parametric estimation. The estimation is based on a doubleindex semi-parametric maximum likelihood with partial observability. The document shows that misspecification error due to an incorrect assumption about the error distribution ma...