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Journal of the ACM (JACM)
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LPAR '08 Proceedings of the 15th International Conference on Logic for Programming, Artificial Intelligence, and Reasoning
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Electronic Notes in Theoretical Computer Science (ENTCS)
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SIAM Journal on Computing
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Runtime analysis of probabilistic programs with unbounded recursion
ICALP'11 Proceedings of the 38th international conference on Automata, languages and programming - Volume Part II
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CAV'11 Proceedings of the 23rd international conference on Computer aided verification
Two variable vs. linear temporal logic in model checking and games
CONCUR'11 Proceedings of the 22nd international conference on Concurrency theory
ISAAC'05 Proceedings of the 16th international conference on Algorithms and Computation
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Information Processing Letters
Model Checking of Recursive Probabilistic Systems
ACM Transactions on Computational Logic (TOCL)
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ATVA'06 Proceedings of the 4th international conference on Automated Technology for Verification and Analysis
Analyzing probabilistic pushdown automata
Formal Methods in System Design
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We present algorithms for the qualitative and quantitative model checking of Linear Temporal Logic (LTL) properties for Recursive Markov Chains (RMCs). Recursive Markov Chains are a natural abstract model of procedural probabilistic programs and related systems involving recursion and probability. For the qualitative problem ("Given a RMC A and an LTL formula \wp, do the computations of A satisfy \wp almost surely?") we present an algorithm that runs in polynomial space in A and exponential time in \wp. For several classes of RMCs, including RMCs with one exit (a special case that corresponds to well-studied probabilistic systems, e.g., multi-type branching processes and stochastic context-free grammars) the algorithm runs in polynomial time in A and exponential time in \wp. On the other hand, we also prove that the problem is EXPTIMEhard, and hence it is EXPTIME-complete. For the quantitative problem ("does the probability that a computation of A satisfies \wp exceed a given threshold p?", or approximate the probability within a desired precision) we present an algorithm that runs in polynomial space in A and exponential space in \wp. For linearly-recursive RMCs, we can compute the exact probability in time polynomial in A and exponential in \wp. These results improve by one exponential, in both the qualitative and quantitative case, the complexity that one would obtain if one first translated the LTL formula to a Buchi automaton and then applied the model checking algorithm for Buchi automata from [11]. Our results combine techniques developed in [10, 11] for analysis of RMCs, and in [6] for LTL model checking of flat Markov Chains