A fundamental problem in text data mining is to extract meaningful structure from document
streams that arrive continuously over time. E-mail and news articles are two natural …

Patterns of deliberate human activity and behavior are of utmost importance in areas as
diverse as disease spread, resource allocation, and emergency response. Because of its …

Time-series of count data are generated in many different contexts, such as web access
logging, freeway traffic monitoring, and security logs associated with buildings. Since this …

For many applications, the data of interest can be best thought of as events—entities that
occur at a particular moment in time, have features and may in turn trigger the occurrence of …

Markov jump processes (or continuous-time Markov chains) are a simple and important
class of continuous-time dynamical systems. In this paper, we tackle the problem of …

Bayesian analysis of complex models based on stochastic processes has in recent years
become a growing area. This book provides a unified treatment of Bayesian analysis of …

The random walk Metropolis (RWM) is one of the most common Markov chain Monte Carlo
algorithms in practical use today. Its theoretical properties have been extensively explored …

This article describes a model based approach to designing network intrusion detection
systems. The article considers general methods applicable to many different types of …

We present an analysis of user conversations in on-line social media and their evolution
over time. We propose a dynamic model that predicts the growth dynamics and structural …

A Markov-modulated Poisson process is a Poisson process whose intensity varies
according to a Markov process. We present a novel technique for simulating from the exact …