Stochastic Block Model Prior for Inclusion Indicators

Specifies a Stochastic Block Model (SBM) prior for inclusion indicators. Variables are assigned to latent clusters, with separate Beta priors on within-cluster and between-cluster inclusion probabilities.

Usage

sbm_prior(
  alpha = 1,
  beta = 1,
  alpha_between = 1,
  beta_between = 1,
  dirichlet_alpha = 1,
  lambda = 1
)

Arguments

alpha

Positive numeric. First shape parameter of the Beta distribution for within-cluster edges. Default: 1.

beta

Positive numeric. Second shape parameter of the Beta distribution for within-cluster edges. Default: 1.

alpha_between

Positive numeric. First shape parameter of the Beta distribution for between-cluster edges. Default: 1.

beta_between

Positive numeric. Second shape parameter of the Beta distribution for between-cluster edges. Default: 1.

dirichlet_alpha

Positive numeric. Concentration parameter of the Dirichlet prior on cluster assignments. Default: 1.

lambda

Positive numeric. Rate parameter of the zero-truncated Poisson prior on the number of clusters. Default: 1.

Value

An object of class "bgms_indicator_prior" with family = "Stochastic-Block".

See also

bernoulli_prior, beta_bernoulli_prior, bgm

Other prior-constructors: bernoulli_prior(), beta_bernoulli_prior(), beta_prime_prior(), cauchy_prior(), exponential_prior(), gamma_prior(), normal_prior()

Examples

sbm_prior()
#> Edge prior: Stochastic-Block
#>   Within:    Beta(1, 1)
#>   Between:   Beta(1, 1)
#>   Dirichlet: 1, Lambda: 1
sbm_prior(alpha = 2, beta = 1, alpha_between = 1, beta_between = 5)
#> Edge prior: Stochastic-Block
#>   Within:    Beta(2, 1)
#>   Between:   Beta(1, 5)
#>   Dirichlet: 1, Lambda: 1