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In this article, a Bayesian model selection approach is introduced that can select the best of a set of inequality and equality constrained hypotheses for contingency tables. The hypotheses are presented in terms of cell probabilities allowing researchers to test (in)equality constrained hypotheses in a format that is directly related to the data. The… (More)

Researchers often have one or more theories or expectations with respect to the outcome of their empirical research. When researchers talk about the expected relations between variables if a certain theory is correct, their statements are often in terms of one or more parameters expected to be larger or smaller than one or more other parameters. Stated… (More)

In this response to Stern's (2005) discussion of Klugkist, Laudy, and Hoijtink (2005), model inference based on posterior probabilities on the parameter space is discussed. Furthermore, the authors respond to Stern's example in which all possible orderings are included via a short discussion of exploratory versus theory-based modeling. Finally, the authors… (More)

Customer temporal behavioral data was represented as images in order to perform churn prediction by leveraging deep learning architectures prominent in image classification. Supervised learning was performed on labeled data of over 6 million customers using deep convolutional neural networks, which achieved an AUC of 0.743 on the test dataset using no more… (More)

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