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Wisconsin - SSC - 3
APPENDIXK - CAUSE OF DEATH CODESThese codes are used with variables: Main: RI166, RI170 01 02 03 04 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 77 97 98 99 Heart disease, heart attack Cancer
Wisconsin - SSC - 3
NSFH3/July 2006 APPENDIX Q - PERSON NUMBERS IN THE NSFH DATA NSFH1 Person Numbers Maintained in NSFH2 In NSFH1 person numbers (also referred to as household member numbers) were assigned to all household members and to all sons and daughters and step
Wisconsin - EX - 3
Evolutionary Biology 2008, Exam 3 Key Page 1 of 71. In order to test whether the rolling-up behavior of roly-poly bugs (terrestrial isopods) is an adaptation to avoid being eaten, you feed several individuals a drug that keeps them from rolling u
Wisconsin - EX - 2
Evolutionary Biology Midterm 2, 2005Page 1 of 7There are 20 questions on this exam; each one will count equally toward your score (none will be dropped). You may have with you at your desk: as many pencils as you need and a calculator. Use the bl
Wisconsin - EX - 2
Evolutionary Biology Midterm 2, 2005Page 1 of 61. Disruptive selection is one of two requirements for sympatric speciation. If there is disruptive selection for the following traits, which trait is most likely to yield sympatric speciation? 0. To
Wisconsin - EX - 3
Evolutionary Biology Exam 3, 2005Page 1 of 71. Upon analysis of the fossil record for a particular area, you find that following the extinction of one group of organisms a second group proliferates. Which of the following does this represent? 0)
Wisconsin - EX - 3
Evolutionary Biology Exam 3, 2005Page 1 of 71. Upon analysis of the fossil record for a particular area, you find that following the extinction of one group of organisms a second group proliferates. Which of the following does this represent? 0)
Wisconsin - EX - 2
Evolutionary Biology Midterm 2, 2006Page 1 of 11There are 20 questions on this exam; each one will count equally toward your score (none will be dropped). You may have with you at your desk: as many pencils as you need and a calculator. Use the b
Wisconsin - EX - 2
Evolutionary Biology Midterm 2, 2006 1. A hallmark of the sensory bias model of sexual selection is that: 0. 1. 2. 3. 4.Page 1 of 7there is a correlation between brothers trait values and their sisters preference for that trait. females that ar
Wisconsin - EX - 3
Evolutionary Biology final exam, 2006Page 1 of 101. Colonization of new habitats and incumbent replacement are expected to increase diversification rate within a lineage because: 0. 1. 2. 3. 4. They make new niches available to the lineage. They
Wisconsin - EX - 3
Evolutionary Biology final exam, 2006Page 1 of 101. Colonization of new habitats and incumbent replacement are expected to increase diversification rate within a lineage because: 0. 1. 2. 3. 4. They make new niches available to the lineage. They
Wisconsin - EX - 2
Evolutionary Biology Midterm 2, 2007 1. You measure proboscis length in a population of proboscis monkeys, and find that the total phenotypic variance is 1643.4, the environmental variance is 943.6, the total genetic variance is 531.1, and the additi
Wisconsin - EX - 2
Evolutionary Biology Midterm 2, 2007 1.Page 1 of 9You measure proboscis length in a population of proboscis monkeys, and find that the total phenotypic variance is 1643.4, the environmental variance is 943.6, the total genetic variance is 531.1,
Wisconsin - EX - 3
Evolutionary Biology Exam 3, 2007 1. As described in this class, the "twofold cost of sex" refers to: 0. 1. 2. 3.Page 1 of 11The fitness cost associated with difficulty in finding a mate. The fact that males only contribute half as many genes to
Wisconsin - ECE - 07
Statistical Decision and Learning TheoryRobert NowakElectrical and Computer Engineering, University of Wisconsin, Madison, USA nowak@engr.wisc.eduAbstract. This paper reviews and contrasts the basic elements of statistical decision theory [14] an
Wisconsin - ECE - 07
ECE901 Spring 2007 Statistical Learning TheoryInstructor: R. NowakLecture 1: Elements of Statistical Learning Theory1. Probabilistic Formulation of learning from data and prediction problems. 2. Performance Characterization: concentration inequ
Wisconsin - ECE - 07
ECE901 Spring 2007 Statistical Learning TheoryInstructor: R. NowakLecture 2: Introduction to Classication and RegressionRecall that the goal of classication is to learn a mapping from the feature space, X , to a label space, Y. This mapping, f ,
Wisconsin - ECE - 07
ECE901 Spring 2007 Statistical Learning TheoryInstructor: R. NowakLecture 3: Introduction to Complexity RegularizationWe ended the previous lecture with a brief discussion of overtting. Recall that, given a set of n data points, Dn , and a space
Wisconsin - ECE - 07
ECE901 Spring 2007 Statistical Learning TheoryInstructor: R. NowakLecture 4: Denoising in Smooth Function SpacesAn example of the use of Sieves for complexity regularization in denoisingConsider the following setting. Let Y = f (X) + W, where
Wisconsin - ECE - 07
ECE901 Spring 2007 Statistical Learning TheoryInstructor: R. NowakLecture 5: Plug-in Classication Rules and Histogram ClassiersWe return to the topic of classication, and we assume an input (feature) space X and a binary output (label) space Y =
Wisconsin - ECE - 07
ECE901 Spring 2007 Statistical Learning TheoryInstructor: R. NowakLecture 6: Probably Approximately Correct (PAC) Learning0.1Overview of the Learning ProblemThe fundamental problem in learning from data is proper Model Selection. As we have
Wisconsin - ECE - 07
ECE901 Spring 2007 Statistical Learning TheoryInstructor: R. NowakLecture 7: Chernos Bound and Hoedings Inequality0.1MotivationIn the last lecture we consider a learning problem in which the optimal function belonged to a nite class of func
Wisconsin - ECE - 07
ECE901 Spring 2007 Statistical Learning TheoryInstructor: R. NowakLecture 8: Classication Error Bounds1Recap: Classier design Given a set of training data {Xi , Yi }n and a nite collection of candidate functions F, select fn F that i=1 (ho
Wisconsin - ECE - 07
ECE901 Spring 2007 Statistical Learning TheoryInstructor: R. NowakLecture 10: Complexity Regularization1Review: PAC BoundsConsider a nite collection of models F, and recall the basic PAC bound: for any > 0, with probability at least 1 R(
Wisconsin - ECE - 07
ECE901 Spring 2007 Statistical Learning TheoryInstructor: R. NowakLecture 11: Decision Trees1Minimum Complexity Penalized FunctionRecall the basic results of the last lectures: let X and Y denote the input and output spaces respectively. Le
Wisconsin - ECE - 07
ECE901 Spring 2007 Statistical Learning TheoryInstructor: R. NowakLecture 12: Complexity Regularization for Squared Error Loss1Complexity Regularization in RegressionThe Cherno/Hoeding bounds were central to our analysis of classier errors.
Wisconsin - ECE - 07
ECE901 Spring 2007 Statistical Learning TheoryInstructor: R. NowakLecture 13: Maximum Likelihood Estimation1Summary of Lecture 12In the last lecture we derived a risk (MSE) bound for regression problems; i.e., select an f F so that E[(f (X
Wisconsin - ECE - 07
ECE901 Spring 2007 Statistical Learning TheoryInstructor: R. NowakLecture 14: Maximum Likelihood and Complexity RegularizationReview : Maximum Likelihood Estimation We have n i.i.d observations drawn from an unknown distribution Yi pi.i.d.,
Wisconsin - ECE - 07
ECE901 Spring 2007 Statistical Learning TheoryInstructor: R. NowakLecture 15: Denoising II Adapting to Unknown Smoothness1Maximum Penalized Likelihood EstimatorsLets recap the last two lectures. Suppose we have data {xi , Yi }n where {xi }
Wisconsin - ECE - 07
ECE901 Spring 2007 Statistical Learning TheoryInstructors: R. Castro and A. SinghLecture 17: Minimax Lower BoundsLower Performance BoundsUp to now in class, weve been analyzing estimators/predictors obtaining upper bounds on their performance.
Wisconsin - ECE - 07
ECE 901 Homework 0: Balancing Estimation and Approximation ErrorsConsider a one-dimensional classication problem in which it is known that the Bayes optimal classier is a simple threshold function. Specically, assume that the input (feature) space i
Wisconsin - ECE - 07
ECE 901 Homework 2Consider the histogram classication rule discussed in Lecture 5. In lecture, we showed that the histogram rule is consistent if the number of bins M and n/M as the number of training data n . The rate of convergence can be arb
Wisconsin - ECE - 07
ECE 901 Homework 31. Consider a classication problem with X = [0, 1]d and Y = {0, 1}. Let F denote the collection of all histogram classiers f : [0, 1]d {0, 1} with M equal volume bins. Assume that minf F R(f ) = 0. For a certain > 0 and > 0, how
Wisconsin - ECE - 07
ECE 901 Homework 41. Consider a classication problem with X = [0, 1]d and Y = {0, 1}. Let F denote the collection of all histogram classiers f : [0, 1]d {0, 1} with M equal volume bins. Do not ssume that minf F R(f ) = 0. For a certain > 0 and >
Wisconsin - ECE - 07
ECE 901 Homework 6 Faster Rates of Convergence for Decision TreesIn Lecture 11 we saw that in two dimensional feature spaces dyadic decision trees yielded a rate of convergence of n1/3 , when the Bayes decision boundary was a one-dimensional curve a
Wisconsin - ECE - 07
ECE 901 Homework 7 - Faster Rates1. Classication in Threshold Classes. Consider a binary classication problem in which the feature space X = [0, 1], and the Bayes classier has the form f (x) = 1xt for a threshold t (0, 1). a. Devise an empirical
Wisconsin - ECE - 07
ECE 901 Homework 81. Complexity Regularization for Parameter Estimation. Suppose make observations according to the following model: Yi = + Wi , i = 1, . . . , n where Rd and {Wi } are iid vectors whose entries are iid random variables with mean
Wisconsin - ECE - 07
ECE 901 Homework 9Many problems in electrical engineering and communications involve the recovery of sinusoidal signals from noisy measurements. Suppose we make observations of the following formKYi =k=1Ak ej2k (i1) + Wi ,i = 1, . . . , n
Wisconsin - ECE - 07
ECE 901 Homework 10Classication in Threshold Classes. Consider a binary classication problem where the feature space is X = [0, 1], and the Bayes classier has the form f (x) = 1{xt } for a threshold t (0, 1). 1. In Homework 7 (question 1b) we cons
Wisconsin - BUSSVC - 2007
What is the correct use of continuity status (Rolling Horizon, Fixed Renewable, Fixed Terminal, On-going, Limited, Acting/Provisional)?o Rolling Horizon A fixed term appointment that extends daily for the term specified. The term may be for one or
Wisconsin - PHYS - 505
PLEASEPOSTChaos and Time-Series Analysis Physics 505: Topics in Physics Fall 2000 - 2 creditsTime & Place: 3:30-5:10 p.m., Tuesdays, 1313 Sterling Hall Instructor: J. C. Sprott, 3285 Chamberlin, 263-4449, sprott@juno.physics.wisc.edu Prerequisites
Wisconsin - STAT - 224
TA: Disccusion: Ofce hour: Class webpage:Shubing Wang shubing@stat.wisc.edu 1:20 - 2:10 pm Wednesday, 474 VAN HISE 11-12 am Monday, MSC 1245, or by appointment http:/www.stat.wisc.edu/ shubing/stat224/Stat 224, Handout 21. Review If events A1 ,
Wisconsin - STAT - 224
Stat 224, Handout 5TA: Shubing Wang, Oct 19, 2005 Summary of Section 3.4 and 3.5 1. Binomial distribution. The number of trials, n; the probability of success on each trial, p; the Binomial random variable X, the number of successes in n trials. The
Wisconsin - STAT - 224
Stat 224, Handout 6TA: Shubing Wang, Oct 26, 2005 1. Poisson Distribution. A random variable X is said to have a Poisson distribution with parameter ( > 0) if the pmf of X is e x , x = 0, 1, 2, p(x; ) = x! So, where is e from? Since we can have M
Wisconsin - STAT - 224
Stat 224, Handout 7TA: Shubing Wang, Nov 2, 2005 Normal Distribution Why normal?0.03 Normal Estimated density 0.0250.020.0150.010.0050010203040506070The pdf of N (, 2 ) is(x)2 1 f (x; , 2 ) = e 22 . 2(1)The cdf o
Wisconsin - STAT - 224
Stat 224, Handout 8TA: Shubing Wang, Nov 9, 2005 Hints and solutions o the practices for Midtern II 1. Let n = number of the people picked. There for X = number of the people with IQ130 is distributed as Bin(n, 0.4). Then nd the minimal n such that
Wisconsin - STAT - 224
Stat 224, Handout 9TA: Shubing Wang, Nov 23, 2005 Review Suppose we observe X1 = x1 , X2 = x2 , , Xn = xn , Xi N (, )1. is known , a 100(1-)% condence interval for the mean is given by x z/2 , x + z/2 , n n2. is unknown a 100(1-)
Wisconsin - STAT - 224
Stat 224, Handout 10TA: Shubing Wang, Nov 30, 2005 1. 15 (b) For large sample, note that s . We can use condence interval for normal distribution. So Z = 2.05 (2.05) 0.98. So we have a 98% C.I. 2. 33 The boxplot isThe R code is # generate a ps
Wisconsin - STAT - 224
Stat 224, Handout 11TA: Shubing Wang, Dec 7, 2005 Summary of Hypothesis Test 1. Denition. Null hypothesis, H0 : some statement; Alternative hypothesis Ha : another statement. 2. Test Statistic. A function of the sample data which is used to make dec
Wisconsin - STAT - 224
Stat 224, Quiz 1 SolutionsShubing Wang, 09/19/05 1. Key point: P (eventA) = (a) P (all 3 are boys) = = = (b) Easy way P (there is at least one girl) = P ({there is no girl}c ) = P ({all 3 are boys}c ) = 1 P ({all 3 are boys}) (using (a) 1 = 1 6 5 =
Wisconsin - STAT - 224
Stat 224, Quiz 4 SolutionsTA: Shubing Wang, 10/27/05 We consider 3 random varibles 1. X: The number of widgets that are worse than standars. 2. Y : The number of widgets that are better than standards 3. Z: The number of widgets that are standards.
Wisconsin - FTI - 423
MWd t ? ? ton tonneDeposit in fuel Total Energy ReleaseEnergy per Fission 169 MeV - FP 5 MeV - n 5 MeV - 12 MeV - FP Decay 8 MeV , Decay -199 MeVtotal fuel ?Just U?
Wisconsin - FTI - 423
Carbide Metallic Oxide2 37 494Liquid Metal Gas11 39Heavy Water Light Water61 439Hf Other Cd AgInCd B Based14 18 34 196 269None Graphite Heavy Water Light Water12 66 60 408Mg Based Steel22 34Zr Based491Concrete Zr Based Low
Wisconsin - FTI - 423
Carbide Metallic Oxide2 37 494 533Liquid Metal Gas11 36Heavy Water Light Water55 428 530Hf Other/Not Stated Cd AgInCd B Based11 31 39 188 261530None Graphite Heavy Water Light Water11 62 55 428 556Not Stated Mg Based Steel11 2