wishartLogLikelihood

Used in calculating model probability in Metropolis-Hastings 
algorithm when proposals are from the Wishart distribution.

Integrative clustering and semi-supervised prediction.
This package includes a 'C++' implementation of both semi-supervised and
unsupervised Multiple Dataset Integration (MDI; Kirk et al., 2012
<https://doi-org.ezp.lib.cam.ac.uk/10.1093/bioinformatics/bts595>), an
extension of Bayesian mixture models to the integrative (multiple dataset or
multi-modal) setting such as multi-omics analysis. The package also includes
Bayesian mixture models. Densities allowed within MDI and the mixture model
are Gaussian with full and diagonal covariance matrices, categorical, Gaussian
with a Gaussian process (GP) prior on the mean parameter. The GP model
implemented canonly handle data with common time points of equal separation.
The Gaussian and GP models can be augmented with a global multivariate t
distribution to handle outliers.

Stephen Coleman

mdir

Bayesian Model-Based Clustering of Multi-Modal Data

Paul Kirk

Chris Wallace

Oliver Crook

wishartLogLikelihood function

<dl><dt>X</dt>
<dd>- matrix; the matrix to calculate the likelihood of.</dd>
<dt>V</dt>
<dd>- matrix; the scale of the Wishart distribution.</dd>
<dt>n</dt>
<dd>- double; the degrees of freedom for the Wishart distribution.</dd>
<dt>P</dt>
<dd>- unsigned integer; the dimension of X.</dd></dl>

Arguments

Wishart log-likelihood — wishartLogLikelihood

<dl>

<dt>X</dt>
<dd>- matrix; the matrix to calculate the likelihood of.</dd>


<dt>V</dt>
<dd>- matrix; the scale of the Wishart distribution.</dd>


<dt>n</dt>
<dd>- double; the degrees of freedom for the Wishart distribution.</dd>


<dt>P</dt>
<dd>- unsigned integer; the dimension of X.</dd>

</dl>

wishartLogLikelihood: Wishart log-likelihood

Description

Usage

Value

Arguments