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Proving expected sensitivity of probabilistic programs

Gilles Barthe, Thomas Espitau, Benjamin Grégoire, Justin Hsu, Pierre-Yves Strub
2017 Proceedings of the ACM on Programming Languages  
We propose an average notion of program sensitivity for probabilistic programs---expected sensitivity---that averages a distance function over a probabilistic coupling of two output distributions from  ...  We develop a relational program logic called EpRHL for proving expected sensitivity properties. Our logic features two key ideas.  ...  Hsu, and Pierre-Yves Strub We consider expected (or average) sensitivity, a natural generalization of sensitivity for the probabilistic setting, and develop a program logic for proving expected sensitivity  ... 
doi:10.1145/3158145 dblp:journals/pacmpl/BartheEGHS18 fatcat:2y3j4jccmnffvp6ytjfus3ra54

Proving Expected Sensitivity of Probabilistic Programs with Randomized Variable-Dependent Termination Time [article]

Peixin Wang, Hongfei Fu, Krishnendu Chatterjee, Yuxin Deng, Ming Xu
2019 arXiv   pre-print
A previous approach develops a relational program logic framework for proving expected sensitivity of probabilistic while loops, where the number of iterations is fixed and bounded.  ...  For probabilistic programs the notion is naturally extended to expected sensitivity.  ...  Yuxi Fu, director of the BA-SICS Lab at Shanghai Jiao Tong University, for his support.  ... 
arXiv:1902.04744v3 fatcat:7463cjox7jcqzjh3qqchd7vgyq

A pre-expectation calculus for probabilistic sensitivity

Alejandro Aguirre, Gilles Barthe, Justin Hsu, Benjamin Lucien Kaminski, Joost-Pieter Katoen, Christoph Matheja
2021 Proceedings of the ACM on Programming Languages (PACMPL)  
This paper is concerned with sensitivity properties of probabilistic programs.  ...  We develop a relational expectation calculus for reasoning about sensitivity of probabilistic computations under the Kantorovich metric.  ...  Part of this research was conducted during the first author's visit to RWTH Aachen University.  ... 
doi:10.18154/rwth-2021-05003 fatcat:igkt32ar3nchthy7rxoxs2xthm

A pre-expectation calculus for probabilistic sensitivity

Alejandro Aguirre, Gilles Barthe, Justin Hsu, Benjamin Lucien Kaminski, Joost-Pieter Katoen, Christoph Matheja
2021 Proceedings of the ACM on Programming Languages (PACMPL)  
This paper is concerned with sensitivity properties of probabilistic programs.  ...  We develop a relational expectation calculus for reasoning about sensitivity of probabilistic computations under the Kantorovich metric.  ...  Part of this research was conducted during the first author's visit to RWTH Aachen University.  ... 
doi:10.1145/3434333 fatcat:xlv22kyqpjeg5bneakh3zn545u

Fairness as a Program Property [article]

Aws Albarghouthi and Loris D'Antoni and Samuel Drews and Aditya Nori
2016 arXiv   pre-print
Second, we discuss an automated verification technique for proving or disproving fairness of decision-making programs with respect to a probabilistic model of the population.  ...  We explore the following question: Is a decision-making program fair, for some useful definition of fairness?  ...  Overview a simple case study and show how techniques for verifying probabilistic programs can be used to automatically prove or disprove global fairness for a class of programs that subsume a range of  ... 
arXiv:1610.06067v1 fatcat:zgyo2xmzzvgnvgjsi4h72qekaa

Probabilistic Program Analysis with Martingales [chapter]

Aleksandar Chakarov, Sriram Sankaranarayanan
2013 Lecture Notes in Computer Science  
We present techniques for the analysis of infinite state probabilistic programs to synthesize probabilistic invariants and prove almost-sure termination.  ...  Next, we present the notion of a super martingale ranking function (SMRF) to prove almost sure termination of probabilistic programs.  ...  (b) We define super martingale ranking functions (SMRFs) to prove almost sure termination of probabilistic programs.  ... 
doi:10.1007/978-3-642-39799-8_34 fatcat:6nqg2nrvkndhfp4473zg4wccfu

Analysis of Bayesian Networks via Prob-Solvable Loops [article]

Ezio Bartocci and Laura Kovács and Miroslav Stankovič
2020 arXiv   pre-print
Prob-solvable loops are probabilistic programs with polynomial assignments over random variables and parametrised distributions, for which the full automation of moment-based invariant generation is decidable  ...  Thanks to these encodings, we can automatically solve several BN related problems, including exact inference, sensitivity analysis, filtering and computing the expected number of rejecting samples in sampling-based  ...  Probabilistic Programs.  ... 
arXiv:2007.09450v2 fatcat:hiqs5qthnneytcf6obmalriwtm

Learning a strategy for adapting a program analysis via bayesian optimisation

Hakjoo Oh, Hongseok Yang, Kwangkeun Yi
2015 Proceedings of the 2015 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications - OOPSLA 2015  
Also, they show that among all program queries that require flow-or context-sensitivity, our partially flow-and context-sensitive analysis answers the 75% of them, while increasing the analysis cost only  ...  The learnt strategy is then used for new, unseen programs. Using our approach, we developed partially flowand context-sensitive variants of a realistic C static analyser.  ...  The right hand side of the equation computes the expectation of this improvement, justifying the name "expected improvement".  ... 
doi:10.1145/2814270.2814309 dblp:conf/oopsla/OhYY15 fatcat:x5yncypgofb35cw6kjlibwxjoy

Learning a strategy for adapting a program analysis via bayesian optimisation

Hakjoo Oh, Hongseok Yang, Kwangkeun Yi
2015 SIGPLAN notices  
Also, they show that among all program queries that require flow-or context-sensitivity, our partially flow-and context-sensitive analysis answers the 75% of them, while increasing the analysis cost only  ...  The learnt strategy is then used for new, unseen programs. Using our approach, we developed partially flowand context-sensitive variants of a realistic C static analyser.  ...  The right hand side of the equation computes the expectation of this improvement, justifying the name "expected improvement".  ... 
doi:10.1145/2858965.2814309 fatcat:osfeez6qyzarrcpueopxq4sqpy

Deadline-Sensitive User Recruitment for Probabilistically Collaborative Mobile Crowdsensing

Mingjun Xiao, Jie Wu, He Huang, Liusheng Huang, Chang Hu
2016 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS)  
In this paper, we focus on the Deadline-sensitive User Recruitment (DUR) problem for probabilistically collaborative mobile crowdsensing, in which mobile users perform sensing tasks with certain probabilities  ...  , and multiple users might be recruited to cooperatively perform a common task, ensuring that the expected completion time be no larger than a deadline.  ...  CONCLUSION We study the DUR problem in the probabilistically collaborative mobile crowdsensing. This is a combining probabilistic set cover problem mixed by non-linear programming.  ... 
doi:10.1109/icdcs.2016.15 dblp:conf/icdcs/XiaoWHHH16 fatcat:3qdipkpannchze2hbwwbarxzgy

Risk-Sensitive Planning with Probabilistic Decision Graphs [chapter]

Sven Koenig, Reid G. Simmons
1994 Principles of Knowledge Representation and Reasoning  
We show the use of probabilistic decision graphs for nding optimal plans for risk-sensitive agents in a stochastic blocksworld domain.  ...  Probabilistic AI planning methods that minimize expected execution cost have a neutral attitude towards risk.  ...  The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the o cial policies, either expressed or implied, of NASA or the U.S. government  ... 
doi:10.1016/b978-1-4832-1452-8.50129-9 fatcat:63vtkfyo3rgaxexh55hv2vyznu

Counterexample-Guided Polynomial Loop Invariant Generation by Lagrange Interpolation [article]

Yu-Fang Chen, Chih-Duo Hong, Bow-Yaw Wang, Lijun Zhang
2015 arXiv   pre-print
We apply multivariate Lagrange interpolation to synthesize polynomial quantitative loop invariants for probabilistic programs.  ...  We reduce the computation of an quantitative loop invariant to solving constraints over program variables and unknown coefficients.  ...  Expectations From an initial program state, a probabilistic program can have different final program states due to probabilistic choice commands.  ... 
arXiv:1502.04280v2 fatcat:2i4yrssftjdovocipsj2rvczji

Weakest Precondition Reasoning for Expected Run–Times of Probabilistic Programs [chapter]

Benjamin Lucien Kaminski, Joost-Pieter Katoen, Christoph Matheja, Federico Olmedo
2016 Lecture Notes in Computer Science  
Its application includes determining the (possibly infinite) expected termination time of a probabilistic program and proving positive almost-sure terminationdoes a program terminate with probability one  ...  This paper presents a wp-style calculus for obtaining bounds on the expected run-time of probabilistic programs.  ...  We thank Gilles Barthe for bringing to our attention the coupon collector problem as a particularly intricate case study for formal verification of expected run-times and Thomas Noll for bringing to our  ... 
doi:10.1007/978-3-662-49498-1_15 fatcat:ij26cc54c5cfphf6jxzlffqp6q

Optimized Tuning Method of PSS Parameters Based on Probabilistic Eigenvalues

Ji-feng LIANG, Long-chao CHEN, Wen-ping HU, Xiao-ming LI, Xiao-jun LI, Xiao-wei WANG
2018 DEStech Transactions on Environment Energy and Earth Science  
This paper proposes a probabilistic eigenvalue sensitivity analysis method. Based on probabilistic eigenvalues, the system stability analysis and PSS parameter selection are performed.  ...  Through the simulation of 8-machine 36-bus system, the results prove that the proposed algorithm and simulation method are effective and rapid, which provides a new method for PSS parameter tuning of power  ...  PSS Siting Process Based on Co-simulation In this paper, when PSASP7.31 is used to control the location of PSS through the interface written by MATLAB software, the sensitivity of the probabilistic feature  ... 
doi:10.12783/dteees/epeee2018/26506 fatcat:jk3hxisqf5fcrpccf5tgdrt3nq

Reasoning Algebraically About Probabilistic Loops [chapter]

Larissa Meinicke, Ian J. Hayes
2006 Lecture Notes in Computer Science  
Our extension is interesting since some well known transformation rules that are applicable to standard programs are not applicable to probabilistic ones: we identify some of these important differences  ...  In particular, our probabilistic action system data refinement rules are new. 1 McIver and Morgan have extended their work on expectation transformer semantics to deal with infinite state spaces [11] .  ...  Probabilistic programs are modeled using expectation transformers.  ... 
doi:10.1007/11901433_21 fatcat:xend4bqv4zaz7mcdyutokrh3ka
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