BACKGROUND Bayesian mixing models have allowed for the inclusion of uncertainty and prior information in the analysis of trophic interactions using stable isotopes. Formulating prior distributions is relatively straightforward when incorporating dietary data. However, the use of data that are related, but not directly proportional, to diet (such as prey availability data) is often problematic b...

Stable isotope mixing models are used to estimate proportional contributions of sources to a mixture, such as in the analysis of animal diets, plant nutrient use, geochemistry, pollution, and forensics. We describe an algorithm implemented as SISUS software for providing a user-specified number of probabilistic exact solutions derived quickly from the extended mixing model. Our method outperfor...

We propose a class of Bayesian nonparametric mixture models with a Beta distribution providing the mixture kernel and a Dirichlet process prior assigned to the mixing distribution. Motivating applications include density estimation on bounded domains, and inference for non-homogeneous Poisson processes over time. We present the mixture model formulation, discuss prior specification, and develop...

This paper utilizes for the first time age-structured human capital data for economic growth forecasting. We concentrate on pooled cross-country data from 58 countries over six five-year periods between 1970 and 2000. We consider specifications chosen by model selection criteria, Bayesian model averaging methodologies based on in-sample and out-of-sample goodness of fit, and on adaptive regress...

We are interested in Bayesian modelling of panel data using a mixed e ects model with heterogeneity in the individual random e ects. We compare two di erent approaches for modelling the heterogeneity using a mixture of Gaussians. In the rst model, we assume an in nite mixture model with a Dirichlet process prior, which is a non-parametric Bayesian model. In the second model, we assume an over-p...

Bayesian generalised ensemble (BayesGE) is a new method that addresses two major drawbacks of standard Markov chain Monte Carlo algorithms for inference in highdimensional probability models: inapplicability to estimate the partition function and poor mixing properties. BayesGE uses a Bayesian approach to iteratively update the belief about the density of states (distribution of the log likelih...

We present the results of a Bayesian analysis of solar neutrino data in terms of νe → νμ,τ oscillations, independent from the Standard Solar Model predictions for the solar neutrino fluxes. We show that such a model independent analysis allows to constraint the values of the neutrino mixing parameters in limited regions around the usual SMA, LMA, LOW and VO regions. Furthermore, there is a stro...