This paper presents a Bayesian nonlinear approach for the analysis of spatial count data. It extends the Bayesian partition methodology of Holmes, Denison, and Mallick (1999, Bayesian partitioning for classification and regression, Technical Report, Imperial College, London) to handle data that involve counts. A demonstration involving incidence rates of leukemia in New York state is used to hi...

An asynchronous stochastic approximation based (Frequentist) approach is proposed for mapping using noisy mobile sensors under two different scenarios: 1) perfectly known sensor locations and 2) uncertain sensor locations. The frequentist methodology has linear complexity in the map components, is immune to the data association problem and is provably consistent. The frequentist methodology, in...

In this article, the authors present a general methodology for age-dependent reliability analysis of degrading or ageing components, structures and systems. The methodology is based on Bayesian methods and inference—its ability to incorporate prior information and on ideas that ageing can be thought of as age-dependent change of beliefs about reliability parameters (mainly failure rate), when c...

Identifying areas prone to flooding is a key step in flood risk management. The purpose of this study develop and present novel susceptibility model based on Bayesian Additive Regression Tree (BART) methodology. predictive performance the new assessed via comparison with Naïve Bayes (NB) Random Forest (RF) methods that were previously published literature. All models tested real case Kan waters...

The Dezert–Smarandache theory (DSmT) and transferable belief model (TBM) both address concerns with the Bayesian methodology as applied to applications involving the fusion of uncertain, imprecise and conflicting information. In this paper, we revisit these concerns regarding the Bayesian methodology in the light of recent developments in the context of the DSmT and TBM. We show that, by exploi...

Posterior expectation is a well-accepted method for data analysis via Bayesian inference based on parametric likelihoods. In this paper we propose utilizing empirical likelihood (EL) methodology to develop novel nonparametric posterior expectation. The parametric Bayesian methodology contains the empirical Bayes approach for the purpose of using the observed data to estimate parameters, or even...

This paper develops a Bayesian methodology for assessing the confidence in model prediction by comparing the model output with experimental data when both are stochastic. The prior distribution of the response is first computed, which is then updated based on experimental observation using Bayesian analysis to compute a validation metric. A model error estimation methodology is then developed t...

In this paper we provide an algorithm allowing to solve the variational Bayesian issue as a functional optimization problem. The main contribution of this paper is to transpose a classical iterative algorithm of optimization in the metric space of probability densities involved in the Bayesian methodology. The main advantage of this methodology is that it allows to address large dimensional inv...

Matrix-variate beta distributions are applied in different fields of hypothesis testing, multivariate correlation analysis, zero regression, canonical correlation analysis and etc. A methodology is proposed to generate matrix-variate beta generator distributions by combining the matrix-variate beta kernel with an unknown function of the trace operator. Several statistical characteristics, exten...