Thematische Bibliographien / Synthesis of Probabilistic Programs

Inhaltsverzeichnis

  1. Zeitschriftenartikel
  2. Dissertationen
  3. Bücher
  4. Buchteile
  5. Konferenzberichte
  6. Berichte der Organisationen

Auswahl der wissenschaftlichen Literatur zum Thema „Synthesis of Probabilistic Programs“

Autor: Grafiati
Veröffentlicht am 28. Juni 2021
Zuletzt aktualisiert am 2. Februar 2022

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Zeitschriftenartikel zum Thema "Synthesis of Probabilistic Programs"

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Nori, Aditya V., Sherjil Ozair, Sriram K. Rajamani, and Deepak Vijaykeerthy. "Efficient synthesis of probabilistic programs." ACM SIGPLAN Notices 50, no. 6 (2015): 208–17. http://dx.doi.org/10.1145/2813885.2737982.

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Salustowicz, Rafal, and Jürgen Schmidhuber. "Probabilistic Incremental Program Evolution." Evolutionary Computation 5, no. 2 (1997): 123–41. http://dx.doi.org/10.1162/evco.1997.5.2.123.

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Probabilistic incremental program evolution (PIPE) is a novel technique for automatic program synthesis. We combine probability vector coding of program instructions, population-based incremental learning, and tree-coded programs like those used in some variants of genetic programming (GP). PIPE iteratively generates successive populations of functional programs according to an adaptive probability distribution over all possible programs. Each iteration, it uses the best program to refine the distribution. Thus, it stochastically generates better and better programs. Since distribution refinem
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Saad, Feras A., Marco F. Cusumano-Towner, Ulrich Schaechtle, Martin C. Rinard, and Vikash K. Mansinghka. "Bayesian synthesis of probabilistic programs for automatic data modeling." Proceedings of the ACM on Programming Languages 3, POPL (2019): 1–32. http://dx.doi.org/10.1145/3290350.

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Satake, Yuki, Hiroshi Unno, and Hinata Yanagi. "Probabilistic Inference for Predicate Constraint Satisfaction." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (2020): 1644–51. http://dx.doi.org/10.1609/aaai.v34i02.5526.

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In this paper, we present a novel constraint solving method for a class of predicate Constraint Satisfaction Problems (pCSP) where each constraint is represented by an arbitrary clause of first-order predicate logic over predicate variables. The class of pCSP properly subsumes the well-studied class of Constrained Horn Clauses (CHCs) where each constraint is restricted to a Horn clause. The class of CHCs has been widely applied to verification of linear-time safety properties of programs in different paradigms. In this paper, we show that pCSP further widens the applicability to verification o
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Lee, Woosuk, Kihong Heo, Rajeev Alur, and Mayur Naik. "Accelerating search-based program synthesis using learned probabilistic models." ACM SIGPLAN Notices 53, no. 4 (2018): 436–49. http://dx.doi.org/10.1145/3296979.3192410.

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Kemper, C. A., N. M. Lane, R. W. Carlson, M. A. Musen, and S. W. Tu. "A Methodology for Determining Patients’ Eligibility for Clinical Trials." Methods of Information in Medicine 32, no. 04 (1993): 317–25. http://dx.doi.org/10.1055/s-0038-1634933.

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AbstractThe task of determining patients’ eligibility for clinical trials is knowledge and data intensive. In this paper, we present a model for the task of eligibility determination, and describe how a computer system can assist clinical researchers in performing that task. Qualitative and probabilistic approaches to computing and summarizing the eligibility status of potentially eligible patients are described. The two approaches are compared, and a synthesis that draws on the strengths of each approach is proposed. The result of applying these techniques to a database of HIV-positive patien
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Chakraborty, Sourav, and Kuldeep S. Meel. "On Testing of Uniform Samplers." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7777–84. http://dx.doi.org/10.1609/aaai.v33i01.33017777.

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Recent years have seen an unprecedented adoption of artificial intelligence in a wide variety of applications ranging from medical diagnosis, automobile industry, security to aircraft collision avoidance. Probabilistic reasoning is a key component of such modern artificial intelligence systems. Sampling techniques form the core of the state of the art probabilistic reasoning systems.
 The divide between the existence of sampling techniques that have strong theoretical guarantees but fail to scale and scalable techniques with weak or no theoretical guarantees mirrors the gap in software en
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Dekhtyar, Alex, and V. S. Subrahmanian. "Hybrid probabilistic programs." Journal of Logic Programming 43, no. 3 (2000): 187–250. http://dx.doi.org/10.1016/s0743-1066(99)00059-x.

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Dix, Jürgen, Mirco Nanni, and V. S. Subrahmanian. "Probabilistic agent programs." ACM Transactions on Computational Logic 1, no. 2 (2000): 208–46. http://dx.doi.org/10.1145/359496.359508.

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Hur, Chung-Kil, Aditya V. Nori, Sriram K. Rajamani, and Selva Samuel. "Slicing probabilistic programs." ACM SIGPLAN Notices 49, no. 6 (2014): 133–44. http://dx.doi.org/10.1145/2666356.2594303.

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Dissertationen zum Thema "Synthesis of Probabilistic Programs"

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Escalante, Marco Antonio. "Probabilistic timing verification and timing analysis for synthesis of digital interface controllers." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0023/NQ36637.pdf.

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Gretz, Friedrich Verfasser], Joost-Pieter [Akademischer Betreuer] [Katoen, and Sriram [Akademischer Betreuer] Sankaranarayanan. "Semantics and loop invariant synthesis for probabilistic programs / Friedrich Gretz ; Joost-Pieter Katoen, Sriram Sankaranarayanan." Aachen : Universitätsbibliothek der RWTH Aachen, 2016. http://d-nb.info/1126278491/34.

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Gretz, Friedrich [Verfasser], Joost-Pieter [Akademischer Betreuer] Katoen, and Sriram [Akademischer Betreuer] Sankaranarayanan. "Semantics and loop invariant synthesis for probabilistic programs / Friedrich Gretz ; Joost-Pieter Katoen, Sriram Sankaranarayanan." Aachen : Universitätsbibliothek der RWTH Aachen, 2016. http://d-nb.info/1126278491/34.

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Schoner, Bernd 1969. "Probabilistic characterization and synthesis of complex driven systems." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/62352.

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Thesis (Ph.D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2000.<br>Includes bibliographical references (leaves 194-204).<br>Real-world systems that have characteristic input-output patterns but don't provide access to their internal states are as numerous as they are difficult to model. This dissertation introduces a modeling language for estimating and emulating the behavior of such systems given time series data. As a benchmark test, a digital violin is designed from observing the performance of an instrument. Cluster-weig
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Stupinský, Šimon. "Pokročilé metody pro syntézu pravděpodobnostních programů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445587.

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Pravdepodobnostné programy zohrávajú rozhodujúcu úlohu v rôznych technických doménach, ako napríklad počítačové siete, vstavané systémy, stratégie riadenia spotreby energie alebo softvérové produčkné linky. PAYNT je nástroj na automatizovanú syntézu pravdepodobnostných programov vyhovujúcich zadaným špecifikáciam. V tejto práci rozširujeme tento nástroj predovšetkým o podporu optimálnej syntézy a syntézy viacerých špecifikácií. Ďalej sme navrhli a implementovali novú metódu, ktorá dokáže efektívne syntetizovať parametre so spojitým definičným oborom ovplyvňujúce pravdepodobnostné prechody popr
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Marcin, Vladimír. "GPU-akcelerovná syntéza pravděpodobnostních programů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445566.

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V tejto práci sa zoberáme problémom automatizovanej syntézy pravdepodobnostných programov: majme konečnú rodinu kandidátnych programov, v ktorej chceme efektívne identifikovať program spĺňajúci danú špecifikáciu. Aj riešenie tých najjednoduchších syntéznych problémov v praxi predstavuje NP-ťažký problém. Pokrok v tejto oblasti prináša nástroj Paynt, ktorý na riešenie tohto problému používa novú integrovanú metódu syntézy pravdepodobnostných programov. Aj keď sa tento prístup dokáže efektívne vysporiadať s exponenciálnym rastom rodín kandidátnych riešení, stále tu existuje problém spôsobený exp
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Angelopoulos, Nicos. "Probabilistic finite domains." Thesis, City University London, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.342823.

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Faria, Francisco Henrique Otte Vieira de. "Learning acyclic probabilistic logic programs from data." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-27022018-090821/.

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To learn a probabilistic logic program is to find a set of probabilistic rules that best fits some data, in order to explain how attributes relate to one another and to predict the occurrence of new instantiations of these attributes. In this work, we focus on acyclic programs, because in this case the meaning of the program is quite transparent and easy to grasp. We propose that the learning process for a probabilistic acyclic logic program should be guided by a scoring function imported from the literature on Bayesian network learning. We suggest novel techniques that lead to orders of magni
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Paige, Timothy Brooks. "Automatic inference for higher-order probabilistic programs." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:d912c4de-4b08-4729-aa19-766413735e2a.

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Probabilistic models used in quantitative sciences have historically co-evolved with methods for performing inference: specific modeling assumptions are made not because they are appropriate to the application domain, but because they are required to leverage existing software packages or inference methods. The intertwined nature of modeling and computational concerns leaves much of the promise of probabilistic modeling out of reach for data scientists, forcing practitioners to turn to off-the-shelf solutions. The emerging field of probabilistic programming aims to reduce the technical and cog
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Crubillé, Raphaëlle. "Behavioural distances for probabilistic higher-order programs." Thesis, Sorbonne Paris Cité, 2019. http://www.theses.fr/2019USPCC084.

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Cette thèse est consacrée à l’étude d’équivalences et de distances comportementales destinées à comparer des programmes probabilistes d’ordre supérieur. Le manuscrit est divisé en trois parties. La première partie consiste en une présentation des langages probabilistes d’ordre supérieur, et des notions d’équivalence et de distance contextuelles pour de tels langages.Dans une deuxième partie, on suit une approche opérationnelle pour construire des notions d’équivalences et de métriques plus simples à manipuler que les notions contextuelles : on prend comme point de départ les deux équivalences
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Bücher zum Thema "Synthesis of Probabilistic Programs"

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Brown, Andrew M. Probabilistic component mode synthesis of nondeterministic substructures. [National Aeronautics and Space Administration, 1997.

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Schmid, Ute. Inductive Synthesis of Functional Programs. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/b12055.

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Kwan, Victor. A predicative model for probabilistic specifications and programs. National Library of Canada, 1998.

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Speight, Vanessa. Probabilistic modeling framework for assessing water quality sampling programs. Water Research Foundation, 2009.

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Pai, Shantaram S. Probabilistic structural analysis of adaptive/smart/intelligent space structures. National Aeronautics and Space Administration, 1991.

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United Kingdom. Department of Social Security. Social Assistance in OECD countries: Synthesis report. HMSO, 1996.

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Bunt, Harry. Advances in Probabilistic and Other Parsing Technologies. Springer Netherlands, 2000.

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Pai, Shantaram S. Probabilistic structural analysis of a truss typical for space station. National Aeronautics and Space Administration, 1990.

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David, Cope. Virtual music: Computer synthesis of musical style. MIT Press, 2001.

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Schroeder, Manfred R. Computer Speech: Recognition, Compression, Synthesis. Springer Berlin Heidelberg, 2004.

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Buchteile zum Thema "Synthesis of Probabilistic Programs"

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Andriushchenko, Roman, Milan Češka, Sebastian Junges, Joost-Pieter Katoen, and Šimon Stupinský. "PAYNT: A Tool for Inductive Synthesis of Probabilistic Programs." In Computer Aided Verification. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81685-8_40.

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AbstractThis paper presents PAYNT, a tool to automatically synthesise probabilistic programs. PAYNT enables the synthesis of finite-state probabilistic programs from a program sketch representing a finite family of program candidates. A tight interaction between inductive oracle-guided methods with state-of-the-art probabilistic model checking is at the heart of PAYNT. These oracle-guided methods effectively reason about all possible candidates and synthesise programs that meet a given specification formulated as a conjunction of temporal logic constraints and possibly including an optimising objective. We demonstrate the performance and usefulness of PAYNT using several case studies from different application domains; e.g., we find the optimal randomized protocol for network stabilisation among 3M potential programs within minutes, whereas alternative approaches would need days to do so.
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Sneyers, Jon, and Danny De Schreye. "Probabilistic Termination of CHRiSM Programs." In Logic-Based Program Synthesis and Transformation. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32211-2_15.

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Klinkenberg, Lutz, Kevin Batz, Benjamin Lucien Kaminski, Joost-Pieter Katoen, Joshua Moerman, and Tobias Winkler. "Generating Functions for Probabilistic Programs." In Logic-Based Program Synthesis and Transformation. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68446-4_12.

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Andriushchenko, Roman, Milan Češka, Sebastian Junges, and Joost-Pieter Katoen. "Inductive Synthesis for Probabilistic Programs Reaches New Horizons." In Tools and Algorithms for the Construction and Analysis of Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72016-2_11.

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AbstractThis paper presents a novel method for the automated synthesis of probabilistic programs. The starting point is a program sketch representing a finite family of finite-state Markov chains with related but distinct topologies, and a reachability specification. The method builds on a novel inductive oracle that greedily generates counter-examples (CEs) for violating programs and uses them to prune the family. These CEs leverage the semantics of the family in the form of bounds on its best- and worst-case behaviour provided by a deductive oracle using an MDP abstraction. The method further monitors the performance of the synthesis and adaptively switches between inductive and deductive reasoning. Our experiments demonstrate that the novel CE construction provides a significantly faster and more effective pruning strategy leading to an accelerated synthesis process on a wide range of benchmarks. For challenging problems, such as the synthesis of decentralized partially-observable controllers, we reduce the run-time from a day to minutes.
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Chasins, Sarah, and Phitchaya Mangpo Phothilimthana. "Data-Driven Synthesis of Full Probabilistic Programs." In Computer Aided Verification. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63387-9_14.

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Abate, Alessandro, Mirco Giacobbe, and Diptarko Roy. "Learning Probabilistic Termination Proofs." In Computer Aided Verification. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81688-9_1.

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AbstractWe present the first machine learning approach to the termination analysis of probabilistic programs. Ranking supermartingales (RSMs) prove that probabilistic programs halt, in expectation, within a finite number of steps. While previously RSMs were directly synthesised from source code, our method learns them from sampled execution traces. We introduce the neural ranking supermartingale: we let a neural network fit an RSM over execution traces and then we verify it over the source code using satisfiability modulo theories (SMT); if the latter step produces a counterexample, we generate from it new sample traces and repeat learning in a counterexample-guided inductive synthesis loop, until the SMT solver confirms the validity of the RSM. The result is thus a sound witness of probabilistic termination. Our learning strategy is agnostic to the source code and its verification counterpart supports the widest range of probabilistic single-loop programs that any existing tool can handle to date. We demonstrate the efficacy of our method over a range of benchmarks that include linear and polynomial programs with discrete, continuous, state-dependent, multi-variate, hierarchical distributions, and distributions with undefined moments.
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Češka, Milan, Christian Hensel, Sebastian Junges, and Joost-Pieter Katoen. "Counterexample-Driven Synthesis for Probabilistic Program Sketches." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30942-8_8.

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Katoen, Joost-Pieter, Friedrich Gretz, Nils Jansen, Benjamin Lucien Kaminski, and Federico Olmedo. "Understanding Probabilistic Programs." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23506-6_4.

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Popescu, Andrei, Johannes Hölzl, and Tobias Nipkow. "Formalizing Probabilistic Noninterference." In Certified Programs and Proofs. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03545-1_17.

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De Raedt, Luc, Kristian Kersting, Angelika Kimmig, Kate Revoredo, and Hannu Toivonen. "Revising Probabilistic Prolog Programs." In Inductive Logic Programming. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73847-3_7.

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Konferenzberichte zum Thema "Synthesis of Probabilistic Programs"

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Nori, Aditya V., Sherjil Ozair, Sriram K. Rajamani, and Deepak Vijaykeerthy. "Efficient synthesis of probabilistic programs." In PLDI '15: ACM SIGPLAN Conference on Programming Language Design and Implementation. ACM, 2015. http://dx.doi.org/10.1145/2737924.2737982.

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Zhang, Yating, Wei Dong, Daiyan Wang, Jiaxin Liu, and Binbin Liu. "Probabilistic Synthesis for Program with Non-API Operations." In 2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C). IEEE, 2020. http://dx.doi.org/10.1109/qrs-c51114.2020.00082.

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Tenenbaum, Joshua. "Reverse-engineering core common sense with the tools of probabilistic programs, game-style simulation engines, and inductive program synthesis." In GECCO '21: Genetic and Evolutionary Computation Conference. ACM, 2021. http://dx.doi.org/10.1145/3449639.3466000.

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Lee, Woosuk, Kihong Heo, Rajeev Alur, and Mayur Naik. "Accelerating search-based program synthesis using learned probabilistic models." In PLDI '18: ACM SIGPLAN Conference on Programming Language Design and Implementation. ACM, 2018. http://dx.doi.org/10.1145/3192366.3192410.

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Spieler, Stefan, Stephan Staudacher, Roland Fiola, Peter Sahm, and Matthias Weißschuh. "Probabilistic Engine Performance Scatter and Deterioration Modeling." In ASME Turbo Expo 2007: Power for Land, Sea, and Air. ASMEDC, 2007. http://dx.doi.org/10.1115/gt2007-27051.

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The change of performance parameters over time due to engine deterioration and production scatter plays an important role to ensure safe and economical engine operation. A tool has been developed which is able to model production scatter and engine deterioration on the basis of elementary changes of numerous construction features. In order to consider the characteristics of an engine fleet as well as random environmental influences, a probabilistic approach using Monte Carlo Simulation (MCS) was chosen. To quantify the impact of feature deviations on performance relevant metrics, non-linear se
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Nandi, Chandrakana, Dan Grossman, Adrian Sampson, Todd Mytkowicz, and Kathryn S. McKinley. "Debugging probabilistic programs." In PLDI '17: ACM SIGPLAN Conference on Programming Language Design and Implementation. ACM, 2017. http://dx.doi.org/10.1145/3088525.3088564.

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Hur, Chung-Kil, Aditya V. Nori, Sriram K. Rajamani, and Selva Samuel. "Slicing probabilistic programs." In PLDI '14: ACM SIGPLAN Conference on Programming Language Design and Implementation. ACM, 2014. http://dx.doi.org/10.1145/2594291.2594303.

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Sankaranarayanan, Sriram, Aleksandar Chakarov, and Sumit Gulwani. "Static analysis for probabilistic programs." In the 34th ACM SIGPLAN conference. ACM Press, 2013. http://dx.doi.org/10.1145/2491956.2462179.

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Cusumano-Towner, Marco, Benjamin Bichsel, Timon Gehr, Martin Vechev, and Vikash K. Mansinghka. "Incremental inference for probabilistic programs." In PLDI '18: ACM SIGPLAN Conference on Programming Language Design and Implementation. ACM, 2018. http://dx.doi.org/10.1145/3192366.3192399.

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Olmedo, Federico, Benjamin Lucien Kaminski, Joost-Pieter Katoen, and Christoph Matheja. "Reasoning about Recursive Probabilistic Programs." In LICS '16: 31st Annual ACM/IEEE Symposium on Logic in Computer Science. ACM, 2016. http://dx.doi.org/10.1145/2933575.2935317.

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Berichte der Organisationen zum Thema "Synthesis of Probabilistic Programs"

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Dantzig, G. B., and G. Infanger. A probabilistic lower bound for two-stage stochastic programs. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/656786.

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Torres, Marissa, Norberto Nadal-Caraballo, and Alexandros Taflanidis. Rapid tidal reconstruction for the Coastal Hazards System and StormSim part II : Puerto Rico and U.S. Virgin Islands. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41482.

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This Coastal and Hydraulics Engineering Technical Note (CHETN) describes the continuing efforts towards incorporating rapid tidal time-series reconstruction and prediction capabilities into the Coastal Hazards System (CHS) and the Stochastic Storm Simulation System (StormSim). The CHS (Nadal-Caraballo et al. 2020) is a national effort for the quantification of coastal storm hazards, including a database and web tool (https://chs.erdc.dren.mil) for the deployment of results from the Probabilistic Coastal Hazard Analysis (PCHA) framework. These PCHA products are developed from regional studies s
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Bonakdarpour, Borzoo, Fuad Abujarad, and Sandeep S. Kulkarni. Parallelizing Deadlock Resolution in Symbolic Synthesis of Distributed Programs. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada487024.

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4

Bonakdarpour, Borzoo, and Sandeep S. Kulkarni. Exploiting Symbolic Techniques in Automated Synthesis of Distributed Programs. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada460390.

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5

Abujarad, Fuad, Borzoo Bonakdarpour, and Sandeep S. Kulkarni. Using Model Checking Techniques for Symbolic Synthesis of Distributed Programs. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada487170.

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6

Torres, Marissa, and Norberto Nadal-Caraballo. Rapid tidal reconstruction with UTide and the ADCIRC tidal database. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41503.

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The quantification of storm surge is vital for flood hazard assessment in communities affected by coastal storms. The astronomical tide is an integral component of the total still water level needed for accurate storm surge estimates. Coastal hazard analysis methods, such as the Coastal Hazards System and the StormSim Coastal Hazards Rapid Prediction System, require thousands of hydrodynamic and wave simulations that are computationally expensive. In some regions, the inclusion of astronomical tides is neglected in the hydrodynamics and tides are instead incorporated within the probabilistic f
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Maslenikov, O. R., J. J. Johnson, L. W. Tiong, M. J. Mraz, S. Bumpus, and M. A. Gerhard. SMACS: a system of computer programs for probabilistic seismic analysis of structures and subsystems. Volume I. User's manual. Office of Scientific and Technical Information (OSTI), 1985. http://dx.doi.org/10.2172/5798909.

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8

Schmid, Ute, and Fritz Wysotzki. Applying Inductive Program Synthesis to Learning Domain-Dependent Control Knowledge - Transforming Plans into Programs. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada382307.

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9

Guerin, David Christopher, Dennis L. Newell, Bruce A. Robinson, Daniel G. Levitt, Leo Van SamBeek, and Gary Callahan. Salt Repository Synthesis Data of Non-Delaware Basin and International Programs for the Storage/Disposal of Nuclear Waste. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1052768.

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Lavoie, D., N. Pinet, S. Zhang, et al. Hudson Bay, Hudson Strait, Moose River, and Foxe basins: synthesis of the research activities under the Geomapping for Energy and Minerals (GEM) programs 2008-2018. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2019. http://dx.doi.org/10.4095/314653.

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