Continuous Time Bayesian Networks

  title={Continuous Time Bayesian Networks},
  author={Uri Nodelman and Christian R. Shelton and Daphne Koller},
In this paper we present a language for finite state continuous time Bayesian networks (CTBNs), which describe structured stochastic processes that evolve over continuous time. The state of the system is decomposed into a set of local variables whose values change over time. The dynamics of the system are described by specifying the behavior of each local variable as a function of its parents in a directed (possibly cyclic) graph. The model specifies, at any given point in time, the… CONTINUE READING
Highly Influential
This paper has highly influenced 46 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 301 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 174 extracted citations

302 Citations

Citations per Year
Semantic Scholar estimates that this publication has 302 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 14 references

The theory of stochastic processes II

  • I. I. Gihman, A. V. Skorohod
  • 1973
Highly Influential
1 Excerpt

The theory of stochastic processes I

  • I. I. Gihman, A. V. Skorohod
  • 1971
Highly Influential
2 Excerpts

On Cox processes and credit risky securities

  • D. Lando
  • Review of Derivatives Research,
  • 1998
3 Excerpts

Recursive valuation of defaultable securities and the timing of resolution of uncertainty

  • D. Duffie, M. Schroder, C. Skiadas
  • The Annals of Applied Probability,
  • 1996
2 Excerpts

Techniques of event history modeling

  • Blossfeld, H.-P, G. Rohwer
  • 1995

A survey of productintegration with a view towards applications in survival analysis

  • R. D. Gill, S. Johansen
  • The Annals of Statistics,
  • 1990
1 Excerpt

Similar Papers

Loading similar papers…