Recent studies of store choice assume that shoppers develop shopping lists a priori, then visit the store that allows them to buy needed items at the lowest expected cost. These studies implicitly assume that shoppers purchase their entire list at a single store. However, many households shop at multiple stores without any intervening consumption—a behavior that retail managers commonly term "c...

In this paper we consider a Bayesian analysis of contingency tables allowing for the possibility that cells may have probability zero. In this sense we depart from standard log-linear modeling that implicitly assumes a positivity constraint. Our approach leads us to consider mixture models for contingency tables, where the components of the mixture, which we call model-instances, have distinct ...

Ellipse detection from a digital image, especially with a complicated background, is still a very challenging problem in image analysis and understanding, and its difficulty relies on how to effectively model these ellipses from edge pixels and locate them automatically. In this paper, we propose a probabilistic mixture model for the probable ellipses in the image and implement a Bayesian Ying-...

Experimental evidence suggests that the content of a memory for even a simple display encoded in visual short-term memory (VSTM) can be very complex. VSTM uses organizational processes that make the representation of an item dependent on the feature values of all displayed items as well as on these items’ representations. Here, we develop a Probabilistic Clustering Theory (PCT) for modeling the...

We propose model-based inference for differential gene expression, using a non-parametric Bayesian probability model for the distribution of gene intensities under different conditions. The probability model is essentially a mixture of normals. The resulting inference is similar to the empirical Bayes approach proposed in Efron et al. (2001). The use of fully modelbased inference mitigates some...

This paper presents a new perspective to the language modeling problem by moving the word representations and modeling into the continuous space. In a previous work we introduced Gaussian-Mixture Language Model (GMLM) and presented some initial experiments. Here, we propose Tied-Mixture Language Model (TMLM), which does not have the model parameter estimation problems that GMLM has. TMLM provid...

We present an unsupervised classiication algorithm based on an ICA mixture model. The ICA mixture model assumes that the observed data can be categorized into several mutually exclusive data classes in which the components in each class are generated by a linear mixture of independent sources. The algorithm nds the independent sources, the mixing matrix for each class and also computes the clas...

Unmotivated students do not reap the full rewards of using a computer-based intelligent tutoring system. Detection of improper behavior is thus an important component of an online student model. To meet this challenge, we present a dynamic mixture model based on Item Response Theory. This model, which simultaneously estimates a student’s proficiency and changing motivation level, was tested wit...

a parallel hybrid system of hmm and gmm modeling techniques was implemented and used in a telephony speaker verification and identification system. spectral subtraction and weighted projection measure were used to render this system more robust against additional noise. cepstral mean subtraction method was also applied for the compensation of convolution noise due to transmission channel degrad...