BPHO: Bayesian Prediction with High-order Interactions

This software can be used in two situations. The first is to predict the next outcome based on the previous states of a discrete sequence. The second is to classify a discrete response based on a number of discreate covariates. In both situations, we use Bayesian logistic regression models that consider the high-order interactions. The models are trained with slice sampling method, a variant of Markov chain Monte Carlo. The time arising from using high-order interactions is reduced greatly by our compression technique that represents a group of original parameters as a single one in MCMC step.

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