Probabilistic models with unknown objects
Probabilistic models with unknown objects
Functional specification of probabilistic process models
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Extending continuous time Bayesian networks
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Monte Carlo methods for tempo tracking and rhythm quantization
Journal of Artificial Intelligence Research
Continuous time particle filtering
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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Probabilistic programming languages allow a modeler to build probabilistic models using complex data structures with all the power of a programming language. We present CTPPL, an expressive probabilistic programming language for dynamic processes that models processes using continuous time. Time is a first class element in our language; the amount of time taken by a subprocess can be specified using the full power of the language. We show through examples that CTPPL can easily represent existing continuous time frameworks and makes it easy to represent new ones. We present semantics for CTPPL in terms of a probability measure over trajectories. We present a particle filtering algorithm for the language that works for a large and useful class of CTPPL programs.