Variational inference for multi-output supervised topic models with hierarchical prior.

The model includes: - LDA structure: theta_d ~ Dir(alpha), phi_k ~ Dir(beta) - Gaussian response: y[d,j] ~ N(zbar_d^T eta_j, sigma_j^2) - Hierarchical prior: eta_j ~ N(mu, Lambda^-1) Lambda ~ inverse-Wishart(upsilon, Omega)

Usage

stm_multi_hier_vi_parallel(
  mod,
  docs,
  y,
  ndsum,
  NZ,
  V,
  K,
  J,
  alpha,
  beta,
  mu,
  upsilon,
  Omega,
  update_sigma = TRUE,
  tau = 20L,
  exact_second_moment = FALSE,
  show_progress = TRUE,
  chunk = 5000L
)

Arguments

mod

List with model state: - nd (D x K) document-topic counts - nw (K x V) topic-word counts - eta (K x J) regression coefficients - sigma2 (J) noise variances

docs

IntegerMatrix (NZ x 3) with (doc_id, word_id, count).

y

NumericMatrix (D x J) response matrix.

ndsum

IntegerVector (D) document token counts.

NZ, V, K, J

Model dimensions.

alpha, beta

Dirichlet hyperparameters.

mu

NumericVector (K) prior mean.

upsilon

Degrees of freedom for inverse-Wishart.

Omega

Scale matrix for inverse-Wishart.

update_sigma

Logical; update sigma2 or not.

tau

Numeric cutoff for stability.

exact_second_moment

Logical flag (currently not used).

show_progress

Logical; print progress.

chunk

Integer; documents per parallel block.

Value

A list with updated variational parameters and diagnostics:

nd

D x K integer matrix of document-topic counts.

nw

K x V integer matrix of topic-word counts.

eta

K x J numeric matrix of regression coefficients.

sigma2

Length-J numeric vector of noise variances.

Lambda_E

K x K numeric matrix, posterior mean of precision matrix Lambda.

IW_upsilon_hat

Numeric scalar, posterior degrees of freedom.

IW_Omega_hat

K x K numeric matrix, posterior scale matrix.

elbo

Numeric scalar, evidence lower bound.

label_loglik

Numeric scalar, supervised log-likelihood term.