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  Browse All Reviews > Mathematics Of Computing (G) > Probability And Statistics (G.3) > Probabilistic Algorithms (Including Monte Carlo) (G.3...)  
 
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  1-10 of 246 Reviews about "Probabilistic Algorithms (Including Monte Carlo) (G.3...)": Date Reviewed
  Practical minimum cut algorithms
Henzinger M., Noe A., Schulz C., Strash D.  Journal of Experimental Algorithmics 231-8, 2018. Type: Article

Due to the ever-increasing deployment of graph theory in natural and artificial phenomena, it is challenging for scientists to utilize it in a more performant manner. One of the most popular topics in graph and networking theory, the authors try t...

Jul 10 2019
  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 of new theo...

Jul 10 2018
  Simulation and the Monte Carlo method (3rd ed.)
Rubinstein R., Kroese D.,  Wiley Publishing, Hoboken, NJ, 2017. 432 pp. Type: Book (978-1-118632-16-1)

Many practical systems fall under the theoretical framework of uncertain dynamical systems, which are then conveniently modeled according to probabilistic or random models. Simulation of such models constitutes a very important aspect of understan...

Jun 27 2018
  Model-based testing of probabilistic systems
Gerhold M., Stoelinga M.  Formal Aspects of Computing 30(1): 77-106, 2018. Type: Article

Gerhold and Stoelinga’s paper proposes an interesting framework to test probabilistic systems based on the concepts of soundness and completeness. The crux of their argument is “the conformance relation for probabilistic input/output c...

Apr 18 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 algorithms can so...

Mar 15 2018
  Analyzing sentiments in one go: a supervised joint topic modeling approach
Hai Z., Cong G., Chang K., Cheng P., Miao C.  IEEE Transactions on Knowledge and Data Engineering 29(6): 1172-1185, 2017. Type: Article

The so-called “crowd wisdom” phenomenon took on a whole new dimension with the advent of the web and the emergence of myriads of online product reviews written by both happy and (more often than not) angry customers. A thorough underst...

Jan 17 2018
  Probability logics: probability-based formalization of uncertain reasoning
Ognjanović Z., Rašković M., Marković Z.,  Springer International Publishing, New York, NY, 2016. 215 pp. Type: Book (978-3-319470-11-5)

Combinations of probability and logic have been a topic of interest since the seminal work of Boole [1], and they have always occupied a place of relevance in foundational studies of probability. However, the direction of development has been cons...

Jun 5 2017
  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 in its “...

Apr 21 2017
  Computer Go
Daniel Bump. YouTube, 00:51:53, published on Aug 2, 2016, Stanford, https://www.youtube.com/watch?v=8B3_UouNZo8. Type: Video

Daniel Bump of Stanford University reviews in this lecture the history of efforts to develop programs that play the ancient Asian game of Go. Such programs have been more difficult than those for chess, due to the greater complexity of pattern rec...

Jan 31 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 alternative ...

Dec 28 2016
 
 
 
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