Center for Automated Learning and Discovery
School of Computer Science, Carnegie Mellon University
Dynamic Nonparametric Bayesian Models
and the Birth-Death Process
Eric P. Xing
Keywords: Dirichlet Process, nonparametric Bayesian models,
birth-death process, Kalman filter, state-space models, longitudinal
When modeling longitudinal data using a set of hidden processes such
as state-space models, a common assumption is that the number of hidden
processes is fixed, and all hidden processes have the same life span
(i.e., all start at the onset of the data stream and terminate at
the end of the data stream). In this report I outline a framework of
modeling complex longitudinal data using a birth-death process, in
which hidden processes and emerge, evolve, and extinct over time.
The model is built on top of a temporally evolving Dirichlet process,
and thus allow the total number of hidden processes to be unbounded.
I also derive a Gibbs sampling algorithm for inference on this model.