Description Usage Arguments Value Author(s) References See Also Examples
estimates the variational posterior distribution of a MPPCA on a data set. A lower bound is calculated and monitored at each iteration. This posterior can be used for various purposes (e.g. MC proposal distribution). It can be transformed using mppcaToGmm and subMppca, outputing a GMM.
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data |
matrix of row-elements. |
ncomp |
number of components in the posterior. |
thres |
threshold for lower bound variations between 2 iterations. Convergence is decided if this variation is below thres. |
maxit |
if NULL, the stopping criterion is related to thres. If not NULL, maxit iterations are performed. |
qmax |
maximal rank of the posterior factor matrices. If NULL, is set to d-1. |
estimated posterior MPPCA with ncomp components.
Pierrick Bruneau
Beal, M. J. (2003) _Variational Algorithms for approximate inference_, PhD Thesis, University of London.
newMppca mppcaToGmm subMppca
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