This paper presents an evolutionary wrapper method for feature selection that uses a non-parametric density estimation method and a Bayesian Classifier. Non-parametric methods are a good alternative for scarce and sparse data, as in Bioinformatics problems, since they do not make any assumptions about its structure and all the information come from data itself. Results show that local modeling ...

In this paper, we deal with building reconstruction in stereoscopic aerial imagery. We present a statistical, and competitive approach to the segmentation of roofs in pre-segmented regions. This parametric method is based on a multi-plane model, interpreted as a Bayesian mixture model. The so-called augmentation of the model with indicator variables allows the using of Bayesian sampler algorith...

A Bayesian semiparametric proportional hazards model is presented to describe the failure behavior of machine tools. The semiparametric setup is introduced using a mixture of Dirichlet processes prior. A Bayesian analysis is performed on real machine tool failure data using the semiparametric setup, and development of optimal replacement strategies are discussed. The results of the semiparametr...

In this paper we derive a Bayesian estimator for doubly correlated MIMO channels. The Bayesian estimator has clearly superior normalized mean squared error performance compared to parametric approaches especially when the channel is strongly correlated. However, since the computational costs may exceed practical limits we present a class of fix point algorithms significantly reducing the numeri...

In a Bayesian approach to online learning a simple approximate parametric form for posterior is updated in each online learning step. Usually in online learning only an estimate of the solution is updated. The Bayesian online approach is applied to two simple learning scenarios, learning a perceptron rule with respectively a spherical and a binary weight prior. In the rst case we rederive the r...

We propose a novel algorithm for extracting the structure of a Bayesian network from a dataset. Our approach is based on generalized conditional entropies, a parametric family of entropies that extends the usual Shannon conditional entropy. Our results indicate that with an appropriate choice of a generalized conditional entropy we obtain Bayesian networks that have superior scores compared to ...

SLAM: cross-species gene finding and alignment with a generalized pair hidden Markov model. Bayesian approach to reconstructing genetic regulatory networks with hidden factors. to analyze expression data. Estimation of genetic networks and functional structures between genes by using Bayesian networks and non-parametric regression. network and nonparametric heteroscedastic regression for nonlin...

The paper gives some basic ideas of both the construction and investigation of the properties of the Bayesian estimates of certain parametric functions of the parent exponential distribution under the model of random censorship assuming the Koziol–Green model. Various prior distributions are investigated and the corresponding estimates are derived. The stress is put on the asymptotic properties...

We study a smoothing spline Poisson regression model for the analysis of mortality data. Being a non-parametric approach it is intrinsically robust, that it is a penalized likelihood estimation method makes available an approximate Bayesian confidence interval and importantly the software gss, its implementation on the freely available statistical package R, makes it easily accessible to the us...

We review the current state of nonparametric Bayesian inference. The discussion follows a list of important statistical inference problems, including density estimation, regression, survival analysis, hierarchical models and model validation. For each inference problem we review relevant nonparametric Bayesian models and approaches including Dirichlet process (DP) models and variations, Polya t...