PPT-Randomized Algorithms

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 . 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 005 p0005 p0005 p0013 p0013 p0013 25 25 25 20 20 20 15 15 15 10 10 10 5 5 5 0 0 0 5 5 5 01 01 01 2345678 2345678 2345678 Change in UDysRS Score LS Mean SE Change in UDysRS Score LS Mean SE Change in UDysRS Score LS Mean SE Weeks Weeks Weeks Placeb trials. Kinza Waqar. Assisitant. Clinical Research Associate. Shifa. Clinical research Center. (SCRC). Contents:. What are RCTs?. General Principals of RCTs. Randomization: The strength of RCTs. Allocation concealment . CS648. . Lecture . 25. Derandomization. using conditional expectation. A probability gem. 1. Derandomization. using . conditional expectation. 2. Problem 1. : Large cut in a graph. Problem:. Let . Complexity of Voting Manipulation Revisited . b. ased on joint work with . Svetlana . Obraztsova. . (NTU/PDMI). and. . Noam . Hazon. . (CMU). Edith Elkind. . (Nanyang. Technological University, Singapore. 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, . Data Collection: . Experiments and Observational Studies. 1/23/12. Association. versus Causation. Confounding Variables. Observational Studies . vs. Experiments. Randomized Experiments. Section 1.3. 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, . James . Aspnes. , Yale. Keren Censor-Hillel, Technion. 1. Snapshot Objects. p. 1. p. 2. p. n. …. …. update(. v. ). scan. 2. update your location. r. ead all locations. Model. 3. System of . n. . . Lecture 2. Randomized Algorithm for Approximate Median. Elementary Probability theory. 1. Randomized Monte Carlo . Algorithm for. . approximate median . 2. This lecture was delivered at slow pace and its flavor was that of a tutorial. . Lower Bounds, and Pseudorandomness. Igor Carboni Oliveira. University of Oxford. Joint work with . Rahul Santhanam. (Oxford). 2. Minor algorithmic improvements imply lower bounds (Williams, 2010).. NEXP. PROOF IN MEDICAL RESEARCH. Susan S. . Ellenberg. , Ph.D.. Department of Biostatistics and Epidemiology. Perelman School of Medicine, U Penn. Statistics is the science of uncertainty. Biostatistics is the application of statistics to biological problems. 10 Bat Algorithms Xin-She Yang, Nature-Inspired Optimization Algorithms, Elsevier, 2014 The bat algorithm (BA) is a bio-inspired algorithm developed by Xin-She Yang in 2010. 10.1 Echolocation of Bats Wendy . Parulekar. MD, FRCP(C). Wei Tu PhD. Objectives. To review the classification of randomized phase II trial designs. To propose and critique potential  randomized phase II trials designs for a concept in head and neck cancer (case scenario to be presented).