Complex systems are often decomposed into modular subsystems for engineering
tractability. Although various equation based white-box modeling techniques make use of …

Intrusion detection systems (IDSs) fall into two high-level categories: network-based systems
(NIDS) that monitor network behaviors, and host-based systems (HIDS) that monitor system …

Continuous-time Bayesian networks is a natural structured representation language for
multicomponent stochastic processes that evolve continuously over time. Despite the …

Dynamic Bayesian networks have been well explored in the literature as discrete-time
models: however, their continuous-time extensions have seen comparatively little attention …

A continuous-time Markov process (CTMP) is a collection of variables indexed by a
continuous quantity, time. It obeys the Markov property that the distribution over a future …

Bayesian networks are powerful statistical models to study the probabilistic relationships
among sets of random variables with significant applications in disease modeling and …

We propose a novel approach toward event detection in real-world continuous video
sequences. The method: 1) is able to model arbitrary-order non-Markovian dependences in …

The continuous time Bayesian network (CTBN) enables temporal reasoning by representing
a system as a factored, finite-state Markov process. The CTBN uses a traditional Bayesian …

The continuous time Bayesian network (CTBN) enables reasoning about complex systems
by representing the system as a factored, finite-state, continuous-time Markov process …

A piecewise-constant conditional intensity model (PCIM) is a non-Markovian model of
temporal stochastic dependencies in continuoustime event streams. It allows efficient …