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Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Michael Mitzenmacher,Eli Upfal +1 more
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Preface 1. Events and probability 2. Discrete random variables and expectation 3. Moments and deviations 4. Chernoff bounds 5. Balls, bins and random graphs 6. Probabilistic method 7. Markov chains and random walks 8. Continuous distributions and the Poisson processAbstract:
Preface 1. Events and probability 2. Discrete random variables and expectation 3. Moments and deviations 4. Chernoff bounds 5. Balls, bins and random graphs 6. The probabilistic method 7. Markov chains and random walks 8. Continuous distributions and the Poisson process 9. Entropy, randomness and information 10. The Monte Carlo method 11. Coupling of Markov chains 12. Martingales 13. Pairwise independence and universal hash functions 14. Balanced allocations References.read more
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Proceedings ArticleDOI
Representative skylines using threshold-based preference distributions
TL;DR: One of the main contributions is to formulate the problem of displaying k representative skyline points such that the probability that a random user would click on one of them is maximized.
Book ChapterDOI
On-the-Fly exact computation of bisimilarity distances
TL;DR: This paper proposes an efficient on-the-fly algorithm which computes exactly the distances between given states and avoids the exhaustive state space exploration, and improves the efficiency of the corresponding iterative algorithms with orders of magnitude.
Journal ArticleDOI
Attestation in Wireless Sensor Networks: A Survey
Rodrigo Vieira Steiner,Emil Lupu +1 more
TL;DR: This article surveys the different approaches to attestation, focusing in particular on those aimed at Wireless Sensor Networks and organises them into a taxonomy, carefully analysing the advantages and disadvantages of each proposal.
Posted Content
Hashing-Based-Estimators for Kernel Density in High Dimensions.
Moses Charikar,Paris Siminelakis +1 more
TL;DR: In this article, the authors study the problem of designing a data structure that given a data set $P$ and a kernel function, returns *approximations to the kernel density* of a query point in *sublinear time.
Journal ArticleDOI
Routing for power minimization in the speed scaling model
TL;DR: This work studies a routing problem with the objective of provisioning guaranteed speed/bandwidth for a given demand matrix while minimizing power consumption, and presents an O((σ/μ)1/α)-approximation, and discusses why coming up with an approximation ratio independent of the startup cost may be hard.
References
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Journal ArticleDOI
The power of two choices in randomized load balancing
TL;DR: This work uses a limiting, deterministic model representing the behavior as n/spl rarr//spl infin/ to approximate the behavior of finite systems and provides simulations that demonstrate that the method accurately predicts system behavior, even for relatively small systems.