M.step: The M-step of teh EM algorithm
In pzwiernik/StructuralEM: Structural EM Algorithm for Gaussian Latent Tree Models
Description
This function performs teh M-step of the EM algorithm.
In the M-step we run chow-liu to get a new update of the tree and the big correlation matrix.
The problem is that the tree does not always make sense so we need to replace it with
a binary tree whose leaves correspond exactly to observed nodes. Luckily such a tree, giving
exactly the same observed likelihood, always exists.
Usage
1
M.step(S)
Arguments
S
covariance matrix over the whole vector (both observed and hidden variables)
pzwiernik/StructuralEM documentation built on May 26, 2019, 11:35 a.m.
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Description
This function performs teh M-step of the EM algorithm. In the M-step we run chow-liu to get a new update of the tree and the big correlation matrix. The problem is that the tree does not always make sense so we need to replace it with a binary tree whose leaves correspond exactly to observed nodes. Luckily such a tree, giving exactly the same observed likelihood, always exists.
Usage
1 | M.step(S)
|
Arguments
S |
covariance matrix over the whole vector (both observed and hidden variables) |
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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