Distributions over matrices with exchangeable rows and infinitely many columns are useful in constructing nonparametric latent variable models. However, the distribution implied by such models over the number of features exhibited by each data point may be poorly-suited for many modeling tasks. In this paper, we propose a class of exchangeable nonparametric priors obtained by restricting the do...

1 In the credit risk analysis, the dependence of default times is one of most important issues, for the portfolio credit derivatives as basket default swaps and CDOs, and also for the contagious credit risks. In the literature, the modelling of multi credit names is diversified in various directions such as Markov models ([3, 4]), contagion models ([10]), latent variable models ([8]) and loss p...

A Bayesian latent variable model is proposed for studying household epidemics of infectious diseases in this paper. This model is more general and flexible than the commonly used chain binomial epidemicmodel. In particular, themodel allows for the heterogeneity of the infection transmission rates in related to the sizes and generations of the infectives. Moreover, the model assumes the availabi...

Automatic satire detection is a subtle text classification task, for machines and at times, even for humans. In this paper we argue that satire detection should be approached using common-sense inferences, rather than traditional text classification methods. We present a highly structured latent variable model capturing the required inferences. The model abstracts over the specific entities app...

Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your perso...

We describe a new method for multivariate discretization based on the use of a latent variable model. The method is proposed as a tool to extend the scope of applicability of machine learning algorithms that handle discrete variables only.

Writing comments about news articles, blogs, or reviews have become a popular activity in social media. In this paper, we analyze reader comments about reviews. Analyzing review comments is important because reviews only tell the experiences and evaluations of reviewers about the reviewed products or services. Comments, on the other hand, are readers’ evaluations of reviews, their questions and...

In this paper we describe a methodology for model-based single channel separation of sounds. We present a sparse latent variable model that can learn sounds based on their distribution of time/frequency energy. This model can then be used to extract known types of sounds from mixtures in two scenarios. One being the case where all sound types in the mixture are known, and the other being being ...

We propose a perceptron-style algorithm for fast discriminative training of structured latent variable model, and analyzed its convergence properties. Our method extends the perceptron algorithm for the learning task with latent dependencies, which may not be captured by traditional models. It relies on Viterbi decoding over latent variables, combined with simple additive updates. Compared to e...