This paper addresses the problem of separating spectral sources which are linearly mixed with unknown proportions. The main difficulty of the problem is to ensure the full additivity (sum-to-one) of the mixing coefficients and non-negativity of sources and mixing coefficients. A Bayesian estimation approach based on Gamma priors was recently proposed to handle the non-negativity constraints in ...

We present a new algorithm for maximum likelihood convolutive independent component analysis (ICA) in which components are unmixed using stable autoregressive filters determined implicitly by estimating a convolutive model of the mixing process. By introducing a convolutive mixing model for the components, we show how the order of the filters in the model can be correctly detected using Bayesia...

Uncertainty associated with mixing models is often substantial, but has not yet been fully incorporated in models. The objective of this study is to develop and apply a Bayesian-mixing model that estimates probability distributions of source contributions to a mixture associated with multiple sources for assessing the uncertainty estimation in sediment fingerprinting in Zidasht catchment, Iran....

SUMMARY. In this paper we propose a semiparamteric Bayesian approach to estimate the mixing function in a mixture of two exponential distributions. Unlike, the traditional mixture of two distributions in this paper we assume that the mixing parameter changes with time. Such models arise naturally in many applications such as software reliability engineering and other related elds. Our proposed ...

background: birth weight and gestational age are two important variables in obstetric research. the primary measure of gestational age is based on a mother's recall of her last menstrual period. this recall may cause random or systematic errors. therefore, the objective of this study is to utilize bayesian mixture model in order to identify implausible gestational age.   methods: in this cross-...

Stable isotopes are a powerful tool for ecologists, often used to assess contributions of different sources to a mixture (e.g. prey to a consumer). Mixing models use stable isotope data to estimate the contribution of sources to a mixture. Uncertainty associated with mixing models is often substantial, but has not yet been fully incorporated in models. We developed a Bayesian-mixing model that ...