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	Browse All Reviews > Mathematics Of Computing (G) > Probability And Statistics (G.3) > Probabilistic Algorithms (Including Monte Carlo) (G.3...)     Options: All Media Types Journals Proceedings Div Books Whole Books Other Date Reviewed Title Author Publisher Published Date Descending Ascending     1-10 of 64 Reviews about "Probabilistic Algorithms (Including Monte Carlo) (G.3...)": Date Reviewed   Probability and computing: randomization and probabilistic techniques in algorithms and data analysis (2nd ed.) Mitzenmacher M., Upfal E., Cambridge University Press, New York, NY, 2017. 484 pp.  Type: Book (978-1-107154-88-9), Reviews: (2 of 2) Probability in computer science plays the same role as in physics: while there is a large corpus of theories and methodologies based on a purely deterministic underpinning, the introduction of probability opens the door to a vast field... Jul 10 2018   Probability and computing: randomization and probabilistic techniques in algorithms and data analysis (2nd ed.) Mitzenmacher M., Upfal E., Cambridge University Press, New York, NY, 2017. 484 pp.  Type: Book (978-1-107154-88-9), Reviews: (1 of 2) It is one of the great paradoxes of modern science that useful computation can be done by making random choices. This insight would be impressive even if the computation in question were contrived, but in many cases probabilistic algor... Mar 15 2018   Bayesian methods in the search for MH370 Davey S., Gordon N., Holland I., Rutten M., Williams J., Springer International Publishing, New York, NY, 2016. 114 pp.  Type: Book One can seldom consider mathematics-heavy books as exciting page-turners. Yet, Bayesian methods in the search for MH370 is so far the best one I have encountered that could stand up to such a description. Published by Springer i... Apr 21 2017   Using evaluation functions in Monte-Carlo tree search Lorentz R. Theoretical Computer Science 644106-113, 2016.  Type: Article Recently, the deep learning paradigm became popular as a consequence of the success of Go game playing. Generally, the investigation of two-player games can be considered a good laboratory for experimenting with algorithms that pursue ... Dec 28 2016   Imprecise random variables, random sets, and Monte Carlo simulation Fetz T., Oberguggenberger M. International Journal of Approximate Reasoning 78(C): 252-264, 2016.  Type: Article The presented work considers the problem of evaluating upper and lower probabilities in systems characterized by imprecise random variables. According to the authors, “Methods of imprecise probability have increasingly attrac... Nov 10 2016   Bayes’ rule: a tutorial introduction to Bayesian analysis Stone J., Sebtel Press, Lexington, KY, 2013. 180 pp.  Type: Book (978-0-956372-84-0) Data is everywhere these days. Big data is certainly a hot topic, but “small data” is often important as well. For instance, in computer vision, there may be a good model for a scene, but parameters of that model ar... Jun 11 2014   Stochastic DAG scheduling using a Monte Carlo approach Zheng W., Sakellariou R. Journal of Parallel and Distributed Computing 73(12): 1673-1689, 2013.  Type: Article Nondeterministic task execution times in heterogeneous distributed systems make task scheduling more challenging. To deal with uncertainty in dynamic environments, the authors of this paper built a prediction-based stochastic task sche... Jan 6 2014   Small variance estimators for rare event probabilities Broniatowski M., Caron V. ACM Transactions on Modeling and Computer Simulation 23(1): 1-23, 2013.  Type: Article This paper talks about improving the sampling estimators in simulation results and catching rare events. Quite interesting discussions of Gaussian samples and approximation validation are also included. In most typical simulation works... Jul 9 2013   Bayesian learning of noisy Markov decision processes Singh S., Chopin N., Whiteley N. ACM Transactions on Modeling and Computer Simulation 23(1): 1-25, 2013.  Type: Article Statistical models are not perfect, but they do help us estimate unknown complex models using fewer parameters. This paper deals with a statistical model fitted to observed actions that originate with a Markov decision process. A new M... Jun 27 2013   Object tracking in the presence of occlusions using multiple cameras: a sensor network approach Ercan A., El Gamal A., Guibas L. ACM Transactions on Sensor Networks 9(2): 1-36, 2013.  Type: Article The nontrivial problem of automating surveillance through a camera sensor-based wireless network, which may involve multiple static and moving occluders, is addressed in this paper.... May 31 2013       Display 510152550100 per page
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