posteriorgmm: Calculates the posterior probability for a given dataset for...
In KSD: Goodness-of-Fit Tests using Kernelized Stein Discrepancy
Description
Usage
Arguments
Value
Examples
Description
Calculates the posterior probability for a given dataset for a GMM
Usage
1
posteriorgmm(model = NULL, X = NULL)
Arguments
model
: The Gaussian Mixture Model
X
(n by d): The dataset of interest,
where n is the number of samples and d is the dimension
Value
P (n by k) : The posterior probabilty of each dataset belonging to each of the k component
Examples
1
2
3
4
5
# compute posterior probability for a default 1-d gaussian mixture model
# and dataset generated from it
model <- gmm()
X <- rgmm(model)
p <- posteriorgmm(model=model, X=X)
Example output
KSD documentation built on Jan. 13, 2021, 8:39 a.m.
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Description Usage Arguments Value Examples
Description
Calculates the posterior probability for a given dataset for a GMM
Usage
1 | posteriorgmm(model = NULL, X = NULL)
|
Arguments
model |
: The Gaussian Mixture Model |
X |
(n by d): The dataset of interest, where n is the number of samples and d is the dimension |
Value
P (n by k) : The posterior probabilty of each dataset belonging to each of the k component
Examples
1 2 3 4 5 | # compute posterior probability for a default 1-d gaussian mixture model
# and dataset generated from it
model <- gmm()
X <- rgmm(model)
p <- posteriorgmm(model=model, X=X)
|
Example output
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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