BernoulliNMF: Collapsed Gibbs Sampler for Bernoulli models
In alumbreras/NBMF: Bayesian Mean-parameterized Nonnegative Binary Matrix Factorization
Description
Usage
Arguments
Value
Description
Samples the auxiliary variables (Z or Z,C) using a collapsed
Gibbs sampler.
Usage
1
2
BernoulliNMF(V, K = K, model = "DirBer", method = "Gibbs", alpha = 1,
beta = 1, gamma = 1, iter = 1000, burnin = 0.5)
Arguments
V
Observed matrix with FxN dimensions
K
Number of latent dimensions
alpha
parameter for the Beta distribution
beta
parameter for the Beta distribution
gamma
parameter for the Dirichlet distribution
iter
number of Gibbs samples
burnin
proportions of initial samples that are ignored for burnin
Value
FxNxJ arrays with samples from the variables
alumbreras/NBMF documentation built on July 28, 2020, 9:58 a.m.
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Description Usage Arguments Value
Description
Samples the auxiliary variables (Z or Z,C) using a collapsed Gibbs sampler.
Usage
1 2 | BernoulliNMF(V, K = K, model = "DirBer", method = "Gibbs", alpha = 1,
beta = 1, gamma = 1, iter = 1000, burnin = 0.5)
|
Arguments
V |
Observed matrix with FxN dimensions |
K |
Number of latent dimensions |
alpha |
parameter for the Beta distribution |
beta |
parameter for the Beta distribution |
gamma |
parameter for the Dirichlet distribution |
iter |
number of Gibbs samples |
burnin |
proportions of initial samples that are ignored for burnin |
Value
FxNxJ arrays with samples from the 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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