One Gibbs sampling sweep for LDA (collapsed) using document–term list.

This function performs a single collapsed Gibbs sampling pass over all non-zero document–term entries. Each (d, v, count) triple is treated as `count` replicated word tokens sharing the same topic assignment.

Usage

eLDA_pass_b_fast(mod, count, ndsum, NZ, V, K, alpha, beta)

Arguments

mod

List with current sampler state: z, nd, nw, and nwsum as described above.

count

IntegerMatrix of size NZ×3, where each row is a triple (d, v, c) with 0-based indices: document index d, word index v, and count c for that (doc, word) pair.

ndsum

IntegerVector of length D; total token count per document (i.e., ndsum[d] = sum_k nd(d,k)). Updated in place.

NZ

Integer, number of non-zero entries (rows in count and length of z).

V

Integer, vocabulary size.

K

Integer, number of topics.

alpha

Scalar Dirichlet prior parameter for document–topic distributions \(\theta_d\) (symmetric).

beta

Scalar Dirichlet prior parameter for topic–word distributions \(\phi_k\) (symmetric).

Value

A list with updated state:

z

Updated topic assignment vector (length NZ).

nd

Updated D×K document–topic counts.

nw

Updated K×V topic–word counts.

nwsum

Updated total word counts per topic.

Details

The state is stored in a list `mod` containing:

z

Integer vector of length NZ; topic assignment for each (d, v, count) triple.

nd

D×K integer matrix; document–topic counts.

nw

K×V integer matrix; topic–word counts.

nwsum

Integer vector of length K; total word count per topic.