This software 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 discreate 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.
Packaged: Thu Feb 21 15:43:05 2008; longhai

Packaged: Fri Feb 22 10:42:21 2008; longhai

Packaged: Wed Apr  2 00:16:31 2008; longhai

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.
Packaged: Mon Apr  7 11:34:26 2008; longhai

Description: 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.