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This R package is 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 discrete covariates. In both situations, we use Bayesian logistic regression models that consider the high-order interactions. 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. In this version, we use log-normal prior for the hyperparameters. When it is used for the second situation --- classification, we consider the full set of interaction patterns up to a specified order.
The technical details are introduced by these references: Li, L., and Neal, R. M. (2008), Compressing Parameters in Bayesian High-order Models with Applications to Sequence Models, Bayesian Analysis, 2008, volume 3, number 4, pp 793 - 822: abstract
Li, L. (2007), Bayesian Classification and Regression with High Dimensional Features, Ph.D. thesis, University of Toronto: abstract
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