Deep dynamic generative models are developed to learn sequential dependencies in time-
series data. The multi-layered model is designed by constructing a hierarchy of temporal …

A gamma process dynamic Poisson factor analysis model is proposed to factorize a dynamic
count matrix, whose columns are sequentially observed count vectors. The model builds a …

We develop deep Poisson-gamma dynamical systems (DPGDS) to model sequentially
observed multivariate count data, improving previously proposed models by not only mining …

Given a query document, ranking the documents in a collection based on how similar they
are to the query is an essential task with extensive applications. For collections that contain …

We propose switching Poisson-gamma dynamical systems (SPGDS) to model sequentially
observed multivariate count data. Different from previous models, SPGDS assigns its latent …

Modeling count-valued time sequences has been receiving growing interests because count
time sequences naturally arise in physical and social domains. Poisson gamma dynamical …

Modeling count-valued time series has been receiving increasing attention since count time
series naturally arise in physical and social domains. Poisson gamma dynamical systems …

Probabilistic approaches for handling count-valued time sequences have attracted amounts
of research attentions because their ability to infer explainable latent structures and to …

We introduce a novel dynamic model for discrete time-series data, in which the temporal
sampling may be nonuniform. The model is specified by constructing a hierarchy of Poisson …

Bayesian nonparametrics is a lively topic in the statistical literature. Thanks to its versatility,
the approach applies to a wide range of modern applications, from machine learning to …