Arbitrary Dependency Mixed Multivariate Bayesian Models
Provides a caret-compatible wrapper around functionality for classific...
Compute a marginal factor (continuous or discrete random variable).
Computes one single factor that is needed for full Bayesian inferencin...
Convert data for usage within Bayesian models.
Transform a collection of Bayesian features back to a sample.
Perform simple (network) Bayesian inferencing and regression.
Full Bayesian inferencing for determining the probability or relative ...
Assign probabilities to one or more samples, given some training data.
Naive Bayesian inferencing for determining the probability or relative...
Assign a probability using a simple (network) Bayesian classifier.
Perform full-dependency Bayesian regression for a sample.
Regression for one or more samples, given some training data.
Perform simple (network) Bayesian regression.
Create a string that can be used in Latex in an e.g. equation-environm...
Given a neighborhood of data, computes the similarity of each sample i...
Validate a Bayesian feature using some sanity checks.
Segment data according to one or more random variables.
Create a Bayesian feature by name and value.
Discretize a continuous random variable to ranges/buckets.
Given a neighborhood of data and two samples from that neighborhood, c...
Safe PDF estimation that works also for sparse random variables.
Get the system-wide default regressor.
Get a boolean indicating whether messages are enabled system-wide.
Get a probability of a discrete value.
Get the range-/bucket-ID of a given value.
Obtain the type of the value of a Bayesian feature.
Obtain the value of a Bayesian feature.
Get a boolean indicating whether warnings are enabled system-wide.
Creates a closure over a variable and returns its getter and setter.
Given Bayesian features, returns those samples from a dataset that exh...
Transform an entire sample into a collection of Bayesian features.
Set a system-wide default regressor.
Enable or disable messages system-wide.
Enable or disable warnings system-wide.
Segment a dataset by each row once, then compute vicinities of samples...
Segment a dataset by a single sample and compute vicinities for it and...
Supports Bayesian models with full and partial (hence arbitrary) dependencies between random variables. Discrete and continuous variables are supported, and conditional joint probabilities and probability densities are estimated using Kernel Density Estimation (KDE). The full general form, which implements an extension to Bayes' theorem, as well as the simple form, which is just a Bayesian network, both support regression through segmentation and KDE and estimation of probability or relative likelihood of discrete or continuous target random variables. This package also provides true statistical distance measures based on Bayesian models. Furthermore, these measures can be facilitated on neighborhood searches, and to estimate the similarity and distance between data points. Related work is by Bayes (1763) <doi:10.1098/rstl.1763.0053> and by Scutari (2010) <doi:10.18637/jss.v035.i03>.