12/29/2023 0 Comments New artifact meaning comp sci![]() Determination automatique de relations lineaires verifiees par les variables d'un programme. University of Pennsylvania Department of Computer and Information Science. Semantic Domains and Denotational Semantics. Construction of Abstract State Graphs with PVS. Church: a language for generative models. Probabilistic machine learning and artificial intelligence. PSI: Exact Symbolic Inference for Probabilistic Programs. Probabilistic Termination: Soundness, Completeness, and Compositionality. In Formal Methods in Compiter-Aided Design (FMCAD'15). Recursive Markov Decision Processes and Recursive Stochastic Games. Recursive Markov Chains, Stochastic Grammars, and Monotone Systems of Nonlinear Equations. on Automata, Langs., and Programming (ICALP'08). Recursive Stochastic Games with Positive Rewards. Mixing Up Nondeterminism and Probability: a preliminary report. Automatic Discovery of Linear Constraints Among Variables of a Program. Systematic Design of Program Analysis Frameworks. In Formal Descriptions of Programming Concepts, (IFIP WG 2.2, St. Static Determination of Dynamic Properties of Recursive Procedures. Abstract Interpretation: A Unified Latice Model for Static Analysis of Programs by Construction or Approximation of Fixpoints. In Program Flow Analysis: Theory and Applications. Semantic Foundations of Program Analysis. Designing a Generic Graph Library Using ML Functors. Bayesian Inference using Data Flow Analysis. Stochastic Invariants for Probabilistic Termination. Algorithmic Analysis of Qualitative and Quantitative Termination Problems for Affine Probabilistic Programs. Termination Analysis of Probabilistic Programs Through Positivstellensatz's. Expectation Invariants for Probabilistic Program Loops as Fixed Points. Probabilistic Program Analysis with Martingales. Stan: A Probabilistic Programming Language. Runtime Analysis of Probabilistic Programs with Unbounded Recursion. Efficient Analysis of Probabilistic Programs with an Unbounded Counter. Efficient Chaotic Iteration Strategies With Widenings. A Lambda-Calculus Foundation for Universal Probabilistic Programming. Formal Certification of Code-based Cryptographic Proofs. Available at justinh.su/files/papers/ellora.pdf. A Program Logic for Probabilistic Programs. Synthesizing Probabilistic Invariants via Doob's Decomposition. Boolean and Cartesian Abstraction for Model Checking C Programs. Formal Methods in System Design 10 (April 1997). Algebraic Decision Diagrams and their Applications. ![]() In Handbook of Logic in Computer Science. Experiments with benchmark programs for the three analyses demonstrate that the approach is practical. Additionally, PMAF has been used to implement a new interprocedural linear expectation-invariant analysis. To evaluate its effectiveness, PMAF has been used to reformulate and implement existing intraprocedural analyses for Bayesian-inference and the Markov decision problem, by creating corresponding interprocedural analyses. One novelty is that PMAF is based on a semantics formulated in terms of a control-flow hyper-graph for each procedure, rather than a standard control-flow graph. To perform interprocedural analysis and to create procedure summaries, PMAF extends ideas from non-probabilistic interprocedural dataflow analysis to the probabilistic setting. PMAF introduces pre-Markov algebras to factor out common parts of different analyses. This paper presents a framework, called PMAF, for designing, implementing, and proving the correctness of static analyses of probabilistic programs with challenging features such as recursion, unstructured control-flow, divergence, nondeterminism, and continuous distributions. Despite recent successes, probabilistic static analyses are still more difficult to design and implement than their deterministic counterparts. While a sampling-based approach-which involves running the program repeatedly-can suggest that ϕ holds, to establish that the program satisfies ϕ, analysis techniques must be used. Automatically establishing that a probabilistic program satisfies some property ϕ is a challenging problem.
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