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.
| Version: | 1.2-5 |
| Depends: | R (≥ 2.5.1) |
| Author: | Longhai Li |
| Maintainer: | Longhai Li <longhai at math.usask.ca> |
| License: | GPL (≥2) |
| URL: | \url{http://www.r-project.org}, \url{http://math.usask.ca/~longhai} |
| In views: | MachineLearning |
| CRAN checks: | BPHO results |
Downloads:
| Package source: | BPHO_1.2-5.tar.gz |
| MacOS X binary: | BPHO_1.2-5.tgz |
| Windows binary: | BPHO_1.2-5.zip |
| Reference manual: | BPHO.pdf |
| Old sources: | BPHO archive |