• Corpus ID: 61284485

Continuous Time Bayesian Networks for Inferring Users’ Presence and Activities with Extensions for Modeling and Evaluation

  title={Continuous Time Bayesian Networks for Inferring Users’ Presence and Activities with Extensions for Modeling and Evaluation},
  author={Uri Nodelman and Eric Horvitz},
Continuous time Bayesian networks (CTBNs) represent structured stochastic processes that evolve over continuous time. The methodology is based on earlier work on homogenous Markov processes, extended to capture dependencies among variables representing continuous time processes. We have worked to apply CTBNs to the challenge of reasoning about users’ presence and availability over time. As part of this research, we extended the methodology of CTBNs to allow a large class of phase distributions… 

Representing Hypoexponential Distributions in Continuous Time Bayesian Networks

This work proposes an extension to support the modeling of the transitioning time as a hypoexponential distribution by introducing an additional hidden variable in continuous time Bayesian networks, and allows CTBNs to obtain memory, which is lacking in standardCTBNs.

Sensitivity Analysis of Continuous Time Bayesian Networks Using Perturbation Realization

  • Computer Science
  • 2012
The perturbation realization method for Markov process sensitivity analysis is extended to the CTBN to build sample paths and compute performance measure derivatives independently for different subnetworks.

Extending Continuous Time Bayesian Networks for Parametric Distributions

By minimizing a modified KL-divergence value, this paper is able to obtain good phasetype approximations for a variety of parametric distributions and proposes and evaluates an extension that uses informed starting locations for the optimization process rather than random initialization.


This prospectus proposes to formalize both uncertain and negative evidence in the context of CTBNs and extend existing inference algorithms to be able to support these new types of evidence, and shows how methods for sensitivity analysis of Markov processes can be applied to the CTBN while exploiting the conditional independence structure of the network.

Tutorial on Structured Continuous-Time Markov Processes

This work introduces continuous-time Markov process representations and algorithms for filtering, smoothing, expected sufficient statistics calculations, and model estimation, and provides the first connection between decision-diagrams and continuous- time Bayesian networks.

Importance Sampling for Continuous Time Bayesian Networks

This paper first presents an approximate inference algorithm based on importance sampling, then extends it to continuous-time particle filtering and smoothing algorithms, and compares them to other approximate algorithms: expectation propagation and Gibbs sampling.

Factored Performance Functions with Structural Representation in Continuous Time Bayesian Networks

For cases in which a performance function must involve multiple nodes, it is shown how to augment the structure of the CTBN to account for the performance interaction while maintaining the factorization of a single performance function for each node.

Making Continuous Time Bayesian Networks More Flexible

This paper generalizes the recently proposed hypoexponential continuous time Bayesian networks, by allowing any number of hypOexponential variables, i.e., variables having a hypoExponential time duration distribution, to be included.

Uncertain and negative evidence in continuous time Bayesian networks



Learning Continuous Time Bayesian Networks

It is shown that CTBNs can provide a better fit to continuous-time processes than DBNs with a fixed time granularity, and can tailor the parameters and dependency structure to the different time granularities of the evolution of different variables.

Continuous Time Bayesian Networks

A probabilistic semantics for the language in terms of the generative model a CTBN defines over sequences of events is presented, and an algorithm for approximate inference which takes advantage of the structure within the process is provided.

Coordinates: Probabilistic Forecasting of Presence and Availability

We present methods employed in COORDINATE, a prototype service that supports collaboration and communication by learning predictive models that provide forecasts of users' presence and availability.