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....

Measurement bias has been defined as a violation of measurement invariance. Potential violators-variables that possibly violate measurement invariance-can be investigated through restricted factor analysis (RFA). The purpose of the present paper is to investigate a Bayesian approach to estimate RFA models with interaction effects, in order to detect uniform and nonuniform measurement bias. Beca...

Classical factor analysis decomposes n observations of dimension p into K(< p) orthogonal factors. In a Bayesian approach we decompose the observation matrix into a product of a factor score and a factor loading matrix of unknown rank by using a normal-Wishart conjugate density family. We assume an informative prior and show how the posterior distribution can be simulated in multivariate blocks...

Factor analysis has been one of the most powerful and flexible tools for assessment of multivariate dependence and codependence. Loosely speaking, it could be argued that the origin of its success rests in its very exploratory nature, where various kinds of data-relationships amongst the variables at study can be iteratively verified and/or refuted. Bayesian inference in factor analytic models ...

An active research topic in machine learning is the development of model structures which would be rich enough to represent relevant aspects of the observations but would still allow efficient learning and inference. Linear factor analysis and related methods such as principal component analysis and independent component analysis are widely used feature extraction and data analysis techniques. ...

A hierarchical Bayesian factor model for multivariate spatially correlated data is proposed. The idea behind the proposed method is to search factor scores incorporating a dependence due to a geographical structure. The great exibility of the Bayesian approach bears directly on the problem of parameter identi cation in factor analysis and furthermore on the inclusion of our prior opinion about ...

Multilevel covariance structure models have become increasingly popular in the psychometric literature in the past few years to account for population heterogeneity and complex study designs. We develop practical simulation based procedures for Bayesian inference of multilevel binary factor analysis models. We illustrate how Markov Chain Monte Carlo procedures such as Gibbs sampling and Metropo...

Although factor analytic models have proven useful for covariance structure modeling and dimensionality reduction in a wide variety of applications, a challenging problem is uncertainty in the number of latent factors. This article proposes an efficient Bayesian approach for model selection and averaging in hierarchical models having one or more factor analytic components. In particular, the ap...

Latent factor models are the canonical statistical tool for exploratory analyses of lowdimensional linear structure for an observation matrix with p features across n samples. We develop a structured Bayesian group factor analysis model that extends the factor model to multiple coupled observation matrices; in the case of two observations, this reduces to a Bayesian model of canonical correlati...