A common goal of unsupervised learning is to discover the latent variables responsible for generating the observed properties of a set of objects. For example, factor analysis attempts to find a set of latent variables (or factors) that explain the correlations among the observed variables. A problem with factor analysis, however, is that the user has to specify the number of latent variables w...

This article provides a Bayesian analysis of the multivariate linear model with polytomous variables. The computational burden due to the intractable multiple integrals induced by the polytomous variables and the model is solved by augmenting the underlying latent continuous measurements of the observed polytomous data. A Gibbs sampler algorithm is implemented to produce the Bayesian estimate. ...

The study of factor analytic models often has to address two important issues: (a) the determination of the ”optimum” number of factors and (b) the derivation of a unique simple structure whose interpretation is easy and straightforward. The classical approach deals with these two tasks separately, and sometimes resorts to ad-hoc methods. This paper proposes a Bayesian approach to these two imp...

A gamma process dynamic Poisson factor analysis model is proposed to factorize a dynamic count matrix, whose columns are sequentially observed count vectors. The model builds a novel Markov chain that sends the latent gamma random variables at time (t − 1) as the shape parameters of those at time t, which are linked to observed or latent counts under the Poisson likelihood. The significant chal...

This paper considers logistic regression analysis with linked data. It is shown that, in logistic regression analysis with linked data, a finite mixture of Bernoulli distributions can be used for modeling the response variables. We proposed an iterative maximum likelihood estimator for the regression coefficients that takes the matching probabilities into account. Next, the Bayesian counterpart...

Bayesian sparse factor analysis has many applications; for example, it has been applied to the problem of inferring a sparse regulatory network from gene expression data. We describe a number of inference algorithms for Bayesian sparse factor analysis using a slab and spike mixture prior. These include well-established Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms as wel...

Many situations exist in which a latent construct has both ordinal and continuous indicators. This presents a problem for the applied researcher because standard measurement models are not designed to accommodate mixed ordinal and continuous data. I address this problem by formulating a measurement model that is appropriate for such mixed multivariate responses. This model unifies standard norm...

There has been recently increasing interest in the establishment of a common currency area in East Asia in the aftermath of the East Asian financial crisis. In this paper, we examine the desirability and feasibility of forming a currency area in the region by checking the symmetry of shocks as an important criterion of the Theory of Optimum Currency Area. We employ a Dynamic Factor Model to dec...