PPT-Randomized Algorithms

Author : liane-varnes | Published Date : 2016-07-07

CS648 Lecture 9 Random Sampling partI Approximating a parameter 1 Overview of the Lecture Randomization Framework for estimation of a parameter Number of balls

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Randomized Algorithms: Transcript


CS648 Lecture 9 Random Sampling partI Approximating a parameter 1 Overview of the Lecture Randomization Framework for estimation of a parameter Number of balls from a bag Size of transitive closure . Randomiza tion if done properly can keep study groups as similar as possible at the outset so that the investigators can isolate and quantify the effect of the interventions they are studying No other study design gives us the power to balance unkno Adversarial Environments. Andreas . Krause. Joint work with . Daniel . Golovin. and . Alex Roper. International Joint Conference on Artificial Intelligence 2011. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: . CS648. . Lecture 3. Two fundamental problems. Balls into bins. Randomized Quick Sort. Random Variable and Expected . value. 1. Balls into BINS. Calculating probability of some interesting events. 2. CS648. . Lecture 15. Randomized Incremental Construction . (building the background). 1. Partition Theorem. A set of events . ,…,. . defined over a probability space (. ,. P. ) is said to induce a partition of . Michael Ben-Or. The Hebrew University. Michael Rabin’s Birthday Celebration. Randomized Protocols. Power of Randomization. Exponential . speedup for known algorithms. Complexity . – The jury is still out . CS648. . Lecture 6. Reviewing the last 3 lectures. Application of Fingerprinting Techniques. 1-dimensional Pattern matching. . Preparation for the next lecture.. . 1. Randomized Algorithms . discussed till now. CS648. . Lecture 17. Miscellaneous applications of . Backward analysis. 1. Minimum spanning tree. 2. Minimum spanning tree. . 3. b. a. c. d. h. x. y. u. v. 18. 7. 1. 19. 22. 10. 3. 12. 3. 15. 11. 5. CS648. . Lecture 4. Linearity of Expectation with applications. (Most important tool for analyzing randomized algorithms). 1. RECAP from the last lecture. 2. Random variable. Definition. :. . A random variable defined over a probability space (. of a graph. Spyros Angelopoulos*. Christoph . Dürr. *. Thomas . Lidbetter**. *. Sorbonne Universités. , UPMC . Univ. Paris 06, CNRS, LIP6, Paris, . France. **Department of Mathematics, London School of Economics, . Neighborhood. Hill Climbing. : Sample p points randomly in the neighborhood of the currently best . solution; determine the best solution of the n sampled points. If it is better than the . current solution, make it the new current solution and continue the search; otherwise, . Prepared by Radcliffe Cardiology. 29 . June 2016. Abbreviations. CTO: . chronic total occlusion. DES: . drug-eluting stent. IVUS. : intravascular ultrasound. LAD. : left anterior descending artery. LCX. This material constitutes supporting material for the "Impact Evaluation in Practice" book. This additional material is made freely but please acknowledge its use as follows: . Gertler. , P. J.; Martinez, S., . What is an Experiment?. Campbell & Stanley stressed random assignment to experimental treatments.. I stress manipulation of the independent variable.. Quasi-Experiments: C&S’s term for research where. Holger Thiele, . MD. o. n behalf of the CULPRIT-SHOCK Investigators. Disclosure Statement of Financial Interest. Grant/Research . Support. Consulting Fees/Honoraria. Major Stock Shareholder/Equity. Royalty Income.

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