randomsamp

List of vectors of predictor levels

predictors

response

Vector of probabilities for sampling each response group

prob

List of <code><a rd-options="" href="/link/bbl?package=bbl&version=1.0.0" data-mini-rdoc="bbl::bbl">bbl</a></code> parameters for each response group;
e.g., generated from calls to <code><a rd-options="" href="/link/randompar?package=bbl&version=1.0.0" data-mini-rdoc="bbl::randompar">randompar</a></code>.

nsample

Predictor-response paired data are generated

Supervised learning using Boltzmann Bayes model inference,
which extends naive Bayes model to include interactions. Enables
classification of data into multiple response groups based on a large
number of discrete predictors that can take factor values of
heterogeneous levels. Either pseudo-likelihood or mean field
inference can be used with L2 regularization, cross-validation, and
prediction on new data.
<doi:10.18637/jss.v101.i05>.

Jun Woo

Boltzmann Bayes Learner

randomsamp function

List of <code><a rd-options='' href='bbl'>bbl</a></code> parameters for each response group;
e.g., generated from calls to <code><a rd-options='' href='randompar'>randompar</a></code>.

randomsamp: Generate Random Boltzmann Bayes Model Data

Description

Usage

Arguments

Value

Details