EM: EM algorithm
In garyganuiuc/BayesEM: Bayesian EM algorithms for Learning the Sparsity of Gaussian Graphical Models
Description
The EM algorithm
Usage
1
Arguments
S
Sample Covariance Matrix
n
Sample Size
p_n
Variable Size
alpha
Tuning Parameter alpha
beta
Tuning Parameter beta
v0
Spike and slab prior v0
v1
Spike and slab prior v1
maxiter
Max iteration
Ra
Constraint on Matrix Range
garyganuiuc/BayesEM documentation built on May 16, 2019, 5:43 p.m.
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Description
The EM algorithm
Usage
1 |
Arguments
S |
Sample Covariance Matrix |
n |
Sample Size |
p_n |
Variable Size |
alpha |
Tuning Parameter alpha |
beta |
Tuning Parameter beta |
v0 |
Spike and slab prior v0 |
v1 |
Spike and slab prior v1 |
maxiter |
Max iteration |
Ra |
Constraint on Matrix Range |
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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