Academic literature on the topic 'Synthesis of Probabilistic Programs'
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Journal articles on the topic "Synthesis of Probabilistic Programs"
Nori, Aditya V., Sherjil Ozair, Sriram K. Rajamani, and Deepak Vijaykeerthy. "Efficient synthesis of probabilistic programs." ACM SIGPLAN Notices 50, no. 6 (August 7, 2015): 208–17. http://dx.doi.org/10.1145/2813885.2737982.
Full textSalustowicz, Rafal, and Jürgen Schmidhuber. "Probabilistic Incremental Program Evolution." Evolutionary Computation 5, no. 2 (June 1997): 123–41. http://dx.doi.org/10.1162/evco.1997.5.2.123.
Full textSaad, 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 (January 2, 2019): 1–32. http://dx.doi.org/10.1145/3290350.
Full textSatake, Yuki, Hiroshi Unno, and Hinata Yanagi. "Probabilistic Inference for Predicate Constraint Satisfaction." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (April 3, 2020): 1644–51. http://dx.doi.org/10.1609/aaai.v34i02.5526.
Full textLee, Woosuk, Kihong Heo, Rajeev Alur, and Mayur Naik. "Accelerating search-based program synthesis using learned probabilistic models." ACM SIGPLAN Notices 53, no. 4 (December 2, 2018): 436–49. http://dx.doi.org/10.1145/3296979.3192410.
Full textKemper, 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.
Full textChakraborty, 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.
Full textDekhtyar, Alex, and V. S. Subrahmanian. "Hybrid probabilistic programs." Journal of Logic Programming 43, no. 3 (June 2000): 187–250. http://dx.doi.org/10.1016/s0743-1066(99)00059-x.
Full textDix, Jürgen, Mirco Nanni, and V. S. Subrahmanian. "Probabilistic agent programs." ACM Transactions on Computational Logic 1, no. 2 (October 2000): 208–46. http://dx.doi.org/10.1145/359496.359508.
Full textHur, Chung-Kil, Aditya V. Nori, Sriram K. Rajamani, and Selva Samuel. "Slicing probabilistic programs." ACM SIGPLAN Notices 49, no. 6 (June 5, 2014): 133–44. http://dx.doi.org/10.1145/2666356.2594303.
Full textDissertations / Theses on the topic "Synthesis of Probabilistic Programs"
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.
Full textGretz, 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.
Full textGretz, 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.
Full textSchoner, Bernd 1969. "Probabilistic characterization and synthesis of complex driven systems." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/62352.
Full textIncludes bibliographical references (leaves 194-204).
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-weighted modeling (CWM), a mixture density estimator around local models, is presented as a framework for function approximation and for the prediction and characterization of nonlinear time series. The general model architecture and estimation algorithm are presented and extended to system characterization tools such as estimator uncertainty, predictor uncertainty and the correlation dimension of the data set. Furthermore a real-time implementation, a Hidden-Markov architecture, and function approximation under constraints are derived within the framework. CWM is then applied in the context of different problems and data sets, leading to architectures such as cluster-weighted classification, cluster-weighted estimation, and cluster-weighted sampling. Each application relies on a specific data representation, specific pre and post-processing algorithms, and a specific hybrid of CWM. The third part of this thesis introduces data-driven modeling of acoustic instruments, a novel technique for audio synthesis. CWM is applied along with new sensor technology and various audio representations to estimate models of violin-family instruments. The approach is demonstrated by synthesizing highly accurate violin sounds given off-line input data as well as cello sounds given real-time input data from a cello player.
by Bernd Schoner.
Ph.D.
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.
Full textMarcin, 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.
Full textAngelopoulos, Nicos. "Probabilistic finite domains." Thesis, City University London, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.342823.
Full textFaria, 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/.
Full textO aprendizado de um programa lógico probabilístico consiste em encontrar um conjunto de regras lógico-probabilísticas que melhor se adequem aos dados, a fim de explicar de que forma estão relacionados os atributos observados e predizer a ocorrência de novas instanciações destes atributos. Neste trabalho focamos em programas acíclicos, cujo significado é bastante claro e fácil de interpretar. Propõe-se que o processo de aprendizado de programas lógicos probabilísticos acíclicos deve ser guiado por funções de avaliação importadas da literatura de aprendizado de redes Bayesianas. Neste trabalho s~ao sugeridas novas técnicas para aprendizado de parâmetros que contribuem para uma melhora significativa na eficiência computacional do estado da arte representado pelo pacote ProbLog. Além disto, apresentamos novas técnicas para aprendizado da estrutura de programas lógicos probabilísticos acíclicos.
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.
Full textCrubillé, Raphaëlle. "Behavioural distances for probabilistic higher-order programs." Thesis, Sorbonne Paris Cité, 2019. http://www.theses.fr/2019USPCC084.
Full textThe present thesis is devoted to the study of behavioral equivalences and distances for higher-order probabilistic programs. The manuscript is divided into three parts. In the first one, higher-order probabilistic languages are presented, as well as how to compare such programs with context equivalence and context distance.The second part follows an operational approach in the aim of building equivalences and metrics easier to handle as their contextual counterparts. We take as starting point here the two behavioral equivalences introduced by Dal Lago, Sangiorgi and Alberti for the probabilistic lambda-calculus equipped with a call-by-name evaluation strategy: the trace equivalence and the bisimulation equivalence. These authors showed that for their language, trace equivalence completely characterizes context equivalence—i.e. is fully abstract, while probabilistic bisimulation is a sound approximation of context equivalence, but is not fully abstract. In the operational part of the present thesis, we show that probabilistic bisimulation becomes fully abstract when we replace the call-by-name paradigm by the call-by-value one. The remainder of this part is devoted to a quantitative generalization of trace equivalence, i.e. a trace distance on programs. We introduce first e trace distance for an affine probabilistic lambda-calculus—i.e. where a function can use its argument at most once, and then for a more general probabilistic lambda-calculus where functions have the ability to duplicate their arguments. In these two cases, we show that these trace distances are fully abstract.In the third part, two denotational models of higher-order probabilistic languages are considered: the Danos and Ehrhard's model based on probabilistic coherence spaces that interprets the language PCF enriched with discrete probabilities, and the Ehrhard, Pagani and Tasson's one based on measurable cones and measurable stable functions that interpret PCF equipped with continuous probabilities. The present thesis establishes two results on these models structure. We first show that the exponential comonad of the category of probabilistic coherent spaces can be expressed using the free commutative comonoid: it consists in a genericity result for this category seen as a model of Linear Logic. The second result clarify the connection between these two models: we show that the category of measurable cones and measurable stable functions is a conservative extension of the co-Kleisli category of probabilistic coherent spaces. It means that the recently introduced model of Ehrhard, Pagani and Tasson can be seen as the generalization to the continuous case of the model for PCF with discrete probabilities in probabilistic coherent spaces
Books on the topic "Synthesis of Probabilistic Programs"
Brown, Andrew M. Probabilistic component mode synthesis of nondeterministic substructures. Washington, DC: [National Aeronautics and Space Administration, 1997.
Find full textSchmid, Ute. Inductive Synthesis of Functional Programs. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/b12055.
Full textKwan, Victor. A predicative model for probabilistic specifications and programs. Ottawa: National Library of Canada, 1998.
Find full textSpeight, Vanessa. Probabilistic modeling framework for assessing water quality sampling programs. Denver, Colo: Water Research Foundation, 2009.
Find full textPai, Shantaram S. Probabilistic structural analysis of adaptive/smart/intelligent space structures. [Washington, DC]: National Aeronautics and Space Administration, 1991.
Find full textUnited Kingdom. Department of Social Security. Social Assistance in OECD countries: Synthesis report. London: HMSO, 1996.
Find full textBunt, Harry. Advances in Probabilistic and Other Parsing Technologies. Dordrecht: Springer Netherlands, 2000.
Find full textPai, Shantaram S. Probabilistic structural analysis of a truss typical for space station. [Washington, DC]: National Aeronautics and Space Administration, 1990.
Find full textDavid, Cope. Virtual music: Computer synthesis of musical style. Cambridge, Mass: MIT Press, 2001.
Find full textSchroeder, Manfred R. Computer Speech: Recognition, Compression, Synthesis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004.
Find full textBook chapters on the topic "Synthesis of Probabilistic Programs"
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, 856–69. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81685-8_40.
Full textSneyers, Jon, and Danny De Schreye. "Probabilistic Termination of CHRiSM Programs." In Logic-Based Program Synthesis and Transformation, 221–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32211-2_15.
Full textKlinkenberg, 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, 231–48. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68446-4_12.
Full textAndriushchenko, 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, 191–209. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72016-2_11.
Full textChasins, Sarah, and Phitchaya Mangpo Phothilimthana. "Data-Driven Synthesis of Full Probabilistic Programs." In Computer Aided Verification, 279–304. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63387-9_14.
Full textAbate, Alessandro, Mirco Giacobbe, and Diptarko Roy. "Learning Probabilistic Termination Proofs." In Computer Aided Verification, 3–26. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81688-9_1.
Full textČeška, Milan, Christian Hensel, Sebastian Junges, and Joost-Pieter Katoen. "Counterexample-Driven Synthesis for Probabilistic Program Sketches." In Lecture Notes in Computer Science, 101–20. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30942-8_8.
Full textKatoen, Joost-Pieter, Friedrich Gretz, Nils Jansen, Benjamin Lucien Kaminski, and Federico Olmedo. "Understanding Probabilistic Programs." In Lecture Notes in Computer Science, 15–32. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23506-6_4.
Full textPopescu, Andrei, Johannes Hölzl, and Tobias Nipkow. "Formalizing Probabilistic Noninterference." In Certified Programs and Proofs, 259–75. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03545-1_17.
Full textDe Raedt, Luc, Kristian Kersting, Angelika Kimmig, Kate Revoredo, and Hannu Toivonen. "Revising Probabilistic Prolog Programs." In Inductive Logic Programming, 30–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73847-3_7.
Full textConference papers on the topic "Synthesis of Probabilistic Programs"
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. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2737924.2737982.
Full textZhang, 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.
Full textTenenbaum, 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. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3449639.3466000.
Full textLee, 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. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3192366.3192410.
Full textSpieler, 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.
Full textNandi, 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. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3088525.3088564.
Full textHur, 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. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2594291.2594303.
Full textSankaranarayanan, Sriram, Aleksandar Chakarov, and Sumit Gulwani. "Static analysis for probabilistic programs." In the 34th ACM SIGPLAN conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2491956.2462179.
Full textCusumano-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. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3192366.3192399.
Full textOlmedo, 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. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2933575.2935317.
Full textReports on the topic "Synthesis of Probabilistic Programs"
Dantzig, G. B., and G. Infanger. A probabilistic lower bound for two-stage stochastic programs. Office of Scientific and Technical Information (OSTI), November 1995. http://dx.doi.org/10.2172/656786.
Full textTorres, 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.), August 2021. http://dx.doi.org/10.21079/11681/41482.
Full textBonakdarpour, Borzoo, Fuad Abujarad, and Sandeep S. Kulkarni. Parallelizing Deadlock Resolution in Symbolic Synthesis of Distributed Programs. Fort Belvoir, VA: Defense Technical Information Center, January 2008. http://dx.doi.org/10.21236/ada487024.
Full textBonakdarpour, Borzoo, and Sandeep S. Kulkarni. Exploiting Symbolic Techniques in Automated Synthesis of Distributed Programs. Fort Belvoir, VA: Defense Technical Information Center, January 2007. http://dx.doi.org/10.21236/ada460390.
Full textAbujarad, Fuad, Borzoo Bonakdarpour, and Sandeep S. Kulkarni. Using Model Checking Techniques for Symbolic Synthesis of Distributed Programs. Fort Belvoir, VA: Defense Technical Information Center, January 2008. http://dx.doi.org/10.21236/ada487170.
Full textTorres, Marissa, and Norberto Nadal-Caraballo. Rapid tidal reconstruction with UTide and the ADCIRC tidal database. Engineer Research and Development Center (U.S.), August 2021. http://dx.doi.org/10.21079/11681/41503.
Full textMaslenikov, 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), March 1985. http://dx.doi.org/10.2172/5798909.
Full textSchmid, Ute, and Fritz Wysotzki. Applying Inductive Program Synthesis to Learning Domain-Dependent Control Knowledge - Transforming Plans into Programs. Fort Belvoir, VA: Defense Technical Information Center, June 2000. http://dx.doi.org/10.21236/ada382307.
Full textGuerin, 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), October 2012. http://dx.doi.org/10.2172/1052768.
Full textLavoie, D., N. Pinet, S. Zhang, J. Reyes, C. Jiang, O. H. Ardakani, M M Savard, 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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