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 nonnegative 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. 
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