'Disjoint' diaporamas de présentation

Phillip B. Gibbons Intel Labs Pittsburgh August 22, 2012

Multi-core Computing Lecture 3 MADALGO Summer School 2012 Algorithms for Modern Parallel and Distributed Models . Phillip B. Gibbons Intel Labs Pittsburgh August 22, 2012. Multi-core Computing Lectures: Progress-to-date on Key Open Questions. How to formally model multi-core hierarchies?

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Probability I

Probability I. Notes. Probability of A occurring P(A) Sum of all possible outcomes = 1. Sample Space. the collection of all possible outcomes of a chance experiment Roll a die S={1,2,3,4,5,6}. # Of Occurrences of Event #Trials Not rolling a even # E C ={1,3,5}.

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Introduction to Probability Theory

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Should a load-balancer choose the path as well as the server?

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The probable is what usually happens. Aristotle

The probable is what usually happens. Aristotle. Chapter 6: Probability. Chance experiment: any activity or situation in which there is uncertainty about which outcome will result. Fate laughs at probabilities. Bulwer, Lytton E.G.

429 views • 26 slides

Probabilistic Horn abduction and Bayesian Networks

Probabilistic Horn abduction and Bayesian Networks. David Poole presented by Hrishikesh Goradia. Introduction. Logic-based systems for diagnostic problems Too many logical possibilities to handle Many of the diagnoses not worth considering Bayesian networks Probabilistic analysis

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David Kauchak cs312

David Kauchak cs312. Review. Midterm. Will be posted online this afternoon You will have 2 hours to take it watch your time! if you get stuck on a problem, move on and come back Must take it by Friday at 6pm You may use: your book your notes the class notes ONLY these things

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Chapter 15

"We figured the odds as best we could, and then we rolled the dice." U.S. President Jimmy Carter, June 10,1976. Chapter 15. Probability Rules!. Useful tools for Probability

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Strings and Languages

Strings and Languages. Jim Skon Mount Vernon Nazarene College. Sequences and Strings. alphabet - a set of symbols , denoted by ? string (or word) - a finite sequence of elements from ? example: ? = {a, b} possible strings include: a aa ab ba abaa bbb aaaa

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STAT E100

STAT E100. Section Week 5 - Probability. Review To best prepare for the exam, pay close attention to your homework and the solutions and take the practice midterms -Also, if you have questions about the grading of your assignment, feel free to contact your grader directly.

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Disjoint Sets

Disjoint Sets. CS2 -- 9/8/2010. Disjoint Sets. A disjoint set contains a set of sets such that in each set, an element is designated as a marker for the set. A simple Disjoint Set: {1}, {2}, {3}, {4}, {5} There is only one marker for each of these sets. The element itself.

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Hierarchical Document Clustering using Frequent Itemsets Benjamin C. M. Fung, Ke Wang, Martin Ester SDM 2003

Hierarchical Document Clustering using Frequent Itemsets Benjamin C. M. Fung, Ke Wang, Martin Ester SDM 2003. Presentation Serhiy Polyakov DSCI 5240 Fall 2005. Introduction. Application of document clustering: web mining search engines information retrieval topological analysis

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Theorem: If topological X t space is compact and Hausdorff, then X t is normal.

Theorem: If topological X t space is compact and Hausdorff, then X t is normal. The proof is quite similar to that of a previous result: a compact subspace of a Hausdorff is closed.

222 views • 9 slides