Introduction

The Model module in pyDPM has included a wide variety of popular PTMs, which can be roughly split into serveral categories: 1) basic topic models;2) deep topic models; 3) sequential topic models; 4) topic model based extensions.

All models are as following:

Probabilistic model list

Type	Probabilistic Model Name	Abbreviation	Paper Link
Basic TM	Latent Dirichlet Allocation	LDA	Link
Basic TM	Poisson Factor Analysis	PFA	Link
Deep TM	Poisson Gamma Belief Network	PGBN	Link
Deep TM	Deep Poisson Factor Analysis	DPFA	Link
Deep TM	Dirichlet Belief Networks	DirBN	Link
Deep TM	Word Embeddings Deep Topic Model	WEDTM	Link
Sequential TM	Convolutional Poisson Factor Analysis	CPFA	Link
Sequential TM	Convolutional Poisson Gamma Belief Network	CPGBN	Link
Sequential TM	Poisson Gamma Dynamical Systems	PGDS	Link
Sequential TM	Deep Poisson Gamma Dynamical Systems	DPGDS	Link
TM based extensions	Multimodal Poisson Gamma Belief Network	MPGBN	Link
TM based extensions	Graph Poisson Gamma Belief Network	GPGBN	Link

More probabilistic models will be further included in pydpm/_model/…