To infer a multilayer representation of high-dimensional count vectors, we propose the Poisson gamma belief network (PGBN) that factorizes each of its layers into the product of a connection weight matrix and the nonnegative real hidden units of the next layer. The PGBN’s hidden layers are jointly trained with an upward-downward Gibbs sampler, each iteration of which upward samples Dirichlet di...

For decades, intelligent tutoring systems researchers have been developing various methods of student modeling. Most of the models, including two of the most popular approaches: Knowledge Tracing model and Performance Factor Analysis, all have similar assumption: the information needed to model the student is the student’s performance. However, there are other sources of information that are no...

Canonical correlation analysis (CCA) is a classical method for seeking correlations between two multivariate data sets. During the last ten years, it has received more and more attention in the machine learning community in the form of novel computational formulations and a plethora of applications. We review recent developments in Bayesian models and inference methods for CCA which are attract...

given the sensitivity of current ground-based gravitational wave (gw) detectors, any continuous-wave signal we can realistically expect will be at a level or below the background noise. hence, any data analysis of detector data will need to rely on statistical techniques to separate the signal from the noise. while with the current sensitivity of our detectors we do not expect to detect any tru...

This paper investigates a factor analysis scheme in the joint channel space of stereo-based stochastic mapping (SSM) for noise robust automatic speech recognition. A mixture of Bayesian factor analyzers is used to describe the generative factors in the multi-conditional training scenario in terms of noise type and signal-to-noise ratio. Sparsity-promoting prior is applied on the matrix of facto...

The main purpose of this article is to develop a Bayesian approach for a general multigroup nonlinear factor analysis model. Joint Bayesian estimates of the factor scores and the structural parameters subjected to some constraints across different groups are obtained simultaneously. A hybrid algorithm that combines the Metropolis-Hastings algorithm and the Gibbs sampler is implemented to produc...