The following are the academic papers we know about that pertain to
continuous time Bayesian networks.  The code assumes knowledge of at least
the first paper which is included in this directory.

BASIC FORMULATION:

Uri Nodelman, Christian R. Shelton, and Daphne Koller
	(2002). "Continuous Time Bayesian Networks." Proceedings
	of the Eighteenth International Conference on
	Uncertainty in Artificial Intelligence (pp. 378-387).
	http://www.cs.ucr.edu/~cshelton/papers/index.cgi?NodSheKol02
[First paper formulating CTBNs.  It defines a CTBN's syntax and semantics
 and briefly discusses an approximate inference method.]

Tal El-Hay, Nir Friedman, Daphne Koller and Raz Kupferman (2006).
	"Continuous Time Markov Networks", Proceedings of the Twenty-Second
	Conference on Uncertainty in Artificial Intelligence (pp. 155-164).
	http://ai.stanford.edu/~koller/Papers/El-Hay+al:UAI06.pdf
[An undirected version of CTBNs that represents the subset of reversable
 Markov processes.]


LEARNING:

Uri Nodelman, Christian R. Shelton, and Daphne Koller (2003). "Learning
	Continuous Time Bayesian Networks." Proceedings of the Nineteenth
	International Conference on Uncertainty in Artificial Intelligence
	(pp.  451-458).
	http://www.cs.ucr.edu/~cshelton/papers/index.cgi?NodSheKol03
[Introduces the sufficient statistics for a CTBN and formulates the
 maximum likelihood and maximum a posteriori parameter learning formulas
 for the complete data case.  It also formulates a structure learning
 method based on a Bayesian scoring function.]

Uri Nodelman, Christian R. Shelton, and Daphne Koller (2005). "Expectation
	Maximization and Complex Duration Distributions for Continuous Time
	Bayesian Networks." Proceedings of the Twenty-First International
	Conference on Uncertainty in Artificial Intelligence (pp. 421-430).
	http://www.cs.ucr.edu/~cshelton/papers/index.cgi?NodSheKol05
[Introduces expectation maximization for CTBNs to extend the parameter
 and structure learning methods of the previous paper to the incomplete
 data case.]


INFERENCE:

(The first paper from 2002 also discusses this to some degree)

Uri Nodelman, Daphne Koller, and Christian R. Shelton (2005). "Expectation
	Propagation for Continuous Time Bayesian Networks." Proceedings of the
	Twenty-First International Conference on Uncertainty in Artificial
	Intelligence (pp. 431-440).
	http://www.cs.ucr.edu/~cshelton/papers/index.cgi?NodKolShe05
[An approximate inference method based on expectation propagation.]

Brenda Ng, Avi Pfeffer, and Richard Dearden (2005). "Continuous Time
	Particle Filtering." Proceedings of the Nineteenth International
	Joint Conference on Artificial Intelligence (pp. 1360-1365).
[A particle filter for the case of an unobserved CTBN that is driving
 stochastic differential equations whose values are observed at
 discrete time points.]

Nir Friedman and Rza Kupferman (2006). "Dimension Reduction in
	Singularly Perturbed Continuous-Time Bayesian Networks."
	Proceedings of the Twenty-Second International
	Conference on Uncertainty in Artificial Intelligence.
	http://www.ma.huji.ac.il/~razk/Publications/PDF/FK06.pdf
[Demonstrates that a separation of time scales between variables can
 lead to a simpler inference problem.]

Suchi Saria, Uri Nodelman, and Daphne Koller (2007). "Reasoning at the
	Right Time Granularity." Proceedings of the Twenty-third Conference
	on Uncertainty in Artificial Intelligence" (pp. 421-430).
	http://ai.stanford.edu/~nodelman/papers/dynamic-EP.pdf
[An improved approximate inference method based on expectation
 propagation.]

Yu Fan and Christian R. Shelton (2008).  "Sampling for Approximate
	Inference in Continuous Time Bayesian Networks."  Proceedings of
	the Tenth International Symposium on Artificial Intelligence
	and Mathematics.
	http://www.cs.ucr.edu/~cshelton/papers/index.cgi?FanShe08
[An importance sampling method and its extension to particle filtering
 and smoothing.]

Tal El-Hay, Nir Friedman, and Raz Kupferman (2008). "Gibbs Sampling in
	Factorized Continuous-Time Markov Processes." Proceedings of the
	Twenty-Fourth Conference on Uncertainty in Artificial Intelligence
	(pp. 169-178).
	http://uai2008.cs.helsinki.fi/UAI_camera_ready/el-hay.pdf
[A method for Gibbs sampling for a CTBN.]

Ido Cohn, Tal El-Hay, Raz Kupferman, and Nir Friedman (2009). "Mean
	Field Variational Approximation for Continuous-Time Bayesian
	Networks."  Proceedings of the Twenty-Fifth International
	Conference on Uncertainty in Artificial Intelligence.
	http://www.cs.mcgill.ca/~uai2009/papers/
		UAI2009_0134_c9c16e478d8e82ecc3848c5dc76b4925.pdf
[A mean field variational method for a CTBN.]

Tal El-Hay, Ido Cohn, Nir Friedman, Raz Kupferman (2010). "Continuous-Time
	Belief Propagation." Proceedings of the Twenty-Seventh International
	Conference on Machine Learning.
	http://www.cs.huji.ac.il/~nir/Papers/ElHayICML10.pdf
[A BP method for a CTBN based on a Bethe energy approximation.]

Vinayak Rao, Yee Whye Teh (2011). "Fast MCMC sampling for Markov jump
	processes and continuous time Bayesian networks." Proceedings of the
	Twenty-Seventh International Conference on Uncertainty in Artificial
	Intelligence.
	http://www.gatsby.ucl.ac.uk/~ywteh/research/compstats/RaoTeh2011a.pdf
[An MCMC method using uniformization.]

E. Busra Celikkaya, Christian R. Shelton, and William Lam (2011). "Factored
	Filtering of Continuous-Time Systems." Proceedings of the
	Twenty-Seventh International Conference on Uncertainty in
	Artificial Intelligence.
	http://www.cs.ucr.edu/~cshelton/papers/index.cgi?CelSheLam11
[A factored matrix-exponential method using uniformization.]
	

OTHER EXTENSIONS:

Karthik Gopalratnam, Henry Kautz, and Daniel S. Weld (2005).  "Extending
	Continuous Time Bayesian Networks." Proceedings of 20th National
	Conference on Artificial Intelligence-AAAI 2005 (pp. 981-986).
	http://www.cs.washington.edu/homes/weld/papers/aaai05_CTBN.pdf
[This presents an alternative to exponential duration distributions.]

Shyamsundar Rajaram, Thore Graepel, and Ralf Herbrich (2005).
	"Poisson-Networks: A Model for Structured Point Processes"
	Proceedings of the Tenth International Workshop on
	Artificial Intelligence and Statistics (pp. 277-284).
	http://www.gatsby.ucl.ac.uk/aistats/fullpapers/200.pdf
[This presents a CTBN-like network for counting variables.]

Kin Fai Kan and Christian R. Shelton (2008). "Solving Structured
	Continuous-Time Markov Decision Processes." Tenth International
	Symposium on Artificial Intelligence and Mathematics.
	http://www.cs.ucr.edu/~cshelton/papers/index.cgi?KanShe08
[This presents an approximate continuous Markov decision process planning
  solution using CTBN.]

Luigi Portinale and Daniele Codetta-Raiteri (2009). "Generalizing
	Continuous Time Bayesian Networks with Immediate Nodes."  
	Proceedings on the Workshop on Graph Structured for Knowledge
	Representation and Reasoning.
	http://web.unipmn.it/%7Eraiteri/papers/gkr.pdf
[Presents an extension for describing simultaneous transitions in a
  CTBN and links it to generalized stochastic Petri nets.]


APPLICATIONS:

(most of the papers above have at least one application of a CTBN.  These
 papers' main contributions are in applications, rather than theory.)

Uri Nodelman and Eric Horvitz (2003). "Continuous Time Bayesian Networks
	for Inferring Users' Presence and Activities with Extensions for
	Modeling and Evaluation."  MSR-TR-2003-97, Microsoft Research.
	http://research.microsoft.com/en-us/um/people/horvitz/uri_eh.pdf
[Applies CTBNs to modeling user's use of desktop applications.]

Ralf Herbrich, Thore Graepel, and Brendan Murphy (2007). "Structure from
	Failure."  SYSML '07: Proceedings of the 2nd USENIX workshop on tackling
	computer system problems with machine learning. pp. 1-6.
	http://www.usenix.org/event/sysml07/tech/full_papers/herbrich/herbrich.pdf
[Applies CTBNs to modeling failures in server farms.]

Jing Xu and Christian R. Shelton (2008). "Continuous Time Bayesian Networks
	for Host Level Network Intrusion Detection." Machine Learning
	and Knowledge Discovery in Databases (ECML/PKDD) (LNAI, vol 5212)
	(pp. 613-627).
	http://www.cs.ucr.edu/~cshelton/papers/index.cgi?XuShe08
[Applies CTBNs to host-level computer network intrusion detection.
 Introduces "toggle" variables.]

Yu Fan and Christian R. Shelton (2009). "Learning Continuous-Time Social
	Network Dynamics." Proceedings of the Twenty-Fifth International Conference
	on Uncertainty in Artificial Intelligence.
	http://www.cs.ucr.edu/~cshelton/papers/index.cgi?YuShe09
[Applies CTBNs to social network dynamics.  Introduces MCMC inference
 method.]
