Description Usage Arguments Details Value Examples
Evaluates the evidence lower bound for a given configuration of variational parameters.
1 | slpm_elbo(X, var_pars, hyper_pars, verbose = F)
|
X |
Rectangular adjacency matrix with non-negative entries. |
var_pars |
A list defining the variational parameters of the model. See Details for more specific indications. |
hyper_pars |
A list defining the hyperparameters of the model. The list should contain three vectors of length |
verbose |
|
The list var_pars
must contain:
M*K
matrix denoting the Gaussian means for senders.
N*K
matrix denoting the Gaussian means for receivers.
M*K
matrix denoting the Gaussian variances for senders.
N*K
matrix denoting the Gaussian variances for receivers.
M*N*K
array representing the soft clustering for the edges. This may be interpreted as the posterior probability that edge ij
is determined by the k
-th latent dimension.
K
dimensional vector containing the variational parameters for the mixing proportions. This may be interpreted as the importance of each latent dimension.
K
dimensional vector containing the shapes of the variational Gamma distributions associated to the precisions.
K
dimensional vector containing the rates of the variational Gamma distributions associated to the precisions.
computing_time |
Number of seconds required for the evaluation. |
elbo |
Value of the ELBO for the given variational parameters. |
1 2 3 4 5 6 7 |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.