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Abstract
The modeling and analysis of probabilistic dynamical systems is becoming a central topic in the formal methods community. Usually, Markov chains of various kinds serve as the core mathematical formalism in these studies. However, in many of these settings, the probabilistic graphical model called dynamic Bayesian networks (DBNs) [4] can be amore appropriate model to work with. This is so since a DBN is often a factored and succinct representation of an underlying Markov chain. Our goal here is to describe DBNs from this standpoint. After introducing the basic formalism, we discuss inferencing algorithms for DBNs. We then consider a simple probabilistic temporal logic and the associated model checking problem for DBNs with a finite time horizon. Finally, we describe how DBNs can be used to study the behavior of biochemical networks.
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School of Computing, National University of Singapore, Singapore
Sucheendra K. Palaniappan & P. S. Thiagarajan
- Sucheendra K. Palaniappan
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Indian Institute of Technology, Computer Science and Engineering, Dept. of Computer Science and Engineering, IIT Bombay, Powai, 400076, Mumbai, Maharashtra, India
Supratik Chakraborty
Chennai Mathematical Institute, H1, SIPCOT IT Park, Kelambakkam, 603103, Siruseri, Tamil Nadu, India
Madhavan Mukund
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Palaniappan, S.K., Thiagarajan, P.S. (2012). Dynamic Bayesian Networks: A Factored Model of Probabilistic Dynamics. In: Chakraborty, S., Mukund, M. (eds) Automated Technology for Verification and Analysis. ATVA 2012. Lecture Notes in Computer Science, vol 7561. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33386-6_2
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