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Building Better Homes. The crying need in our culture is not better houses… it’s better homes. Proverbs 15:16-17 & 17:1. 2 Ptr . 3 “seeing that all these things shall be dissolved…”. 2 Ptr . 3 “seeing that all these things shall be dissolved…”.

Randomized algorithm. Tutorial 1 Hint for homework 1. Outline. Lookup table problem (Exercise 1.18) Convex function (Exercise 2.10) Hint for homework 1. Lookup table problem. F :{0,…, n -1}→{0,…, m -1}. For any x and y with 0≦ x , y ≦ n- 1:

MEMORY MANAGEMENT. IM PM MM 3 SERVICES OF OS MM. MM. Keep track of all Mem locns free (F) or allocated (Al) & if al to which PR.. & how much Decide Mem allocn. Policy – which PR. should get how much Mem, when & where

Evaluation Notes Bergen COURSE Spring, 2010. Petra Todd University of Pennsylvania Department of Economics. The Evaluation Problem. Will study econometric methods for evaluating effects of active labor market programs Employment, training and job search assistance programs

情報セキュリティ特論（ 3 ）. 黒澤　馨 （茨城大学） kurosawa@mx.ibaraki.ac.jp. 演習 (1). One-time pad について考える。 鍵 K=(001101) で、平文 M=(111111) を 暗号化せよ。 C=K+M =(001101)+(111111) =(110010). 演習 (1). One-time pad について考える。 鍵 K=(001101) で、暗号文 C=(101101) を 復号せよ。 M=K+C =(001101)+(101101)

Robust Ranking of Uncertain Data. Da Yan and Wilfred Ng The Hong Kong University of Science and Technology. Outline. Background Probabilistic Data Model Related Work U-Pop k Semantics U-Pop k Algorithm Experiments Conclusion. Background.

Finite Model Theory Lecture 18. Extended 0/1 Laws Or “Getting Real”. Outline. A better probabilistic model Probabilities of conjunctive queries Probabilities for FO Based on work done with N. Dalvi and G.Miklau, and on papers by Lynch, Shelah and Spencer. Annomalies 0/1 Laws.

Performance of a diagnostic test. Dagmar Rimek EPIET-EUPHEM Introductory Course 2012 Lazareto, Menorca, Spain. Based on the Lecture of 2011 by Steen Ethelberg. Outline. Performance characteristics of a test Sensitivity Specificity Choice of a threshold

?. Heb.13:4. Gen.2:24. “disposable-marriage” “ wegwerp huwelijk ”. “I thought things would be different” “Ik dacht dat de dingen anders zouden zijn” “I want to do what I want to do” “ Ik wil doen wat ik wil ” “I’m not in love anymore” “ Ik ben niet meer verliefd ”

Probabilities in Databases and Logics I. Nilesh Dalvi and Dan Suciu University of Washington. Two Lectures. Today: probabilistic database to model imprecisions probabilistic logics Tomorrow: probabilistic database to model incompletness random graphs. Motivation. Record reconciliation

Probabilistic Testability Measures. Student: Shih-Chieh Wu Advisor: Chun-Yao Wang 2005.12.26 Department of Computer Science National Tsing Hua University, Taiwan. Outline. Introduction Controllability Calculation with Aliasing-Free Assignments Conclusion & Future Work. Introduction.

ACTIVITES MENTALES. Préparez-vous !. Collège Jean Monnet. Question 1. Factoriser : 49 + 56x + 16x². Calculer l’aire du disque :. Question 2. 4 cm. R. Question 3. 5 cm. ?. U. 7 cm. V. Calculer RUV à 1° près. Enoncer la définition du livret correspondant à cette figure :.

CS723 - Probability and Stochastic Processes. Lecture No. 07. In Previous Lectures.

?. Heb.13:4. Gen.2:24. “disposable-marriage” “ wegwerp huwelijk ”. “I thought things would be different” “Ik dacht dat de dingen anders zouden zijn” “I want to do what I want to do” “ Ik wil doen wat ik wil ” “I’m not in love anymore” “ Ik ben niet meer verliefd ”

Derandomizing BPP. Slides by Iddo Tzameret and Gil Shklarski. Adapted from Oded Goldreich’s course lecture notes by Erez Waisbard and Gera Weiss. PRG - Stronger Notion. Def :

3 Causal Models Part II: Counterfactual Theory and Traditional Approaches to Confounding (Bias?). Confounding, Identifiability, Collapsibility and Causal inference. Thursday reception at lunch time at SACEMA. Review Yesterday. Causes – definition Sufficient causes model Component causes

Stock and Watson, Exercise 2.3. (a) Compute E(Y). We first need to find the density of y, p(y). Using formulas discussed in class (i.e., recovering the marginal density from the joint), we find Pr(Y=0) = .045 + .005 = .05 Pr(Y=1) = .709 + .241 = .95

Novel Approaches to Adjusting for Confounding: Propensity Scores, Instrumental Variables and MSMs. Matthew Fox Advanced Epidemiology. What are the exposures you are interested in studying?.

Introduction to Biostatistics (ZJU 2008). Wenjiang Fu, Ph.D Associate Professor Division of Biostatistics, Department of Epidemiology Michigan State University East Lansing, Michigan 48824, USA Email: fuw@msu.edu www: http://www.msu.edu/~fuw. Chapters 4-5 Probability distribution.

Lecture 7. Basic statistical modeling. The Chinese University of Hong Kong CSCI3220 Algorithms for Bioinformatics. Lecture outline. Introduction to statistical modeling Motivating examples Generative and discriminative models Classification and regression Bayes and Naïve Bayes classifiers