A Bayesian statistical model and estimation methodology based on forward projection adaptive Markov chain Monte Carlo is developed in order to perform the calibration of a high-dimensional nonlinear system of ordinary differential equations representing an epidemic model for human papillomavirus types 6 and 11 (HPV-6, HPV-11). The model is compartmental and involves stratification by age, gende...

We provide user friendly software for Bayesian analysis of functional data models using WinBUGS 1.4. The excellent properties of Bayesian analysis in this context are due to: (1) dimensionality reduction, which leads to low dimensional projection bases; (2) mixed model representation of functional models, which provides a modular approach to model extension; and (3) orthogonality of the princip...

BACKGROUND Stable isotope analysis is increasingly being utilised across broad areas of ecology and biology. Key to much of this work is the use of mixing models to estimate the proportion of sources contributing to a mixture such as in diet estimation. METHODOLOGY By accurately reflecting natural variation and uncertainty to generate robust probability estimates of source proportions, the ap...

a numerical study of 3d electrokinetic flows through micromixers was performed. the micromixers considered here consisted of heterogeneous rectangular microchannels with prescribed patterns of zeta-potential at their walls. numerical simulation of electroosmotic flows within heterogeneous channels requires solution of the navier-stokes, ernest-plank and species concentration equations. it is kn...

one of the methods used in the analysis of data related to diseases, and their underlying reasons is drawing geographical map. mapping diseases is a valuable tool to determine the regions of high rate of infliction requiring therapeutic interventions. the objective of this study was to investigate obesity pattern in iran by drawing geographical maps based on bayesian spatial model to recognize ...

This paper presents a nonlinear mixing model for hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are post-nonlinear functions of unknown pure spectral components contaminated by an additive white Gaussian noise. The nonlinear effects are approximated by a polynomial leading to a polynomial post-nonlinear mixing model. A Bayesian algorithm is proposed to esti...

Bayesian single index model is a highly promising dimension reduction tool for an interpretable modeling of the non linear relationship between the response and its predictors. However, existing Bayesian tools in this area suffer from slow mixing of the Markov Chain Monte Carlo (MCMC) computational tool and also lack the ability to deal with missing covariates. To circumvent these practical pro...