  We develop dependent hierarchical normalized random measures and apply them
to dynamic topic modeling. The dependency arises via superposition, subsampling
and point transition on the underlying Poisson processes of these measures. The
measures used include normalised generalised Gamma processes that demonstrate
power law properties, unlike Dirichlet processes used previously in dynamic
topic modeling. Inference for the model includes adapting a recently developed
slice sampler to directly manipulate the underlying Poisson process.
Experiments performed on news, blogs, academic and Twitter collections
demonstrate the technique gives superior perplexity over a number of previous
models.
