Statistics Department
Course List for Fall 2012/Spring 2013

Revised: 14 January 2013
Courses whose numbers end with a are offered in the FALL. Courses whose numbers end with b are offered in the SPRING.
Courses whose numbers end with ab are offered both semesters. Courses with a gray background are not taught this year.

CourseNumberInstructorTimeRoom
Introduction to Statistics 101a-106a/501a-506a Jonathan Reuning-Scherer and Staff Tues, Thurs 1:00-2:15 OML 202
Probability and Statistics 238a/538a Joseph Chang Tues, Thurs 1:00-2:15 ML 211
Probability Theory with Applications 241a/541a/MATH 241a Harrison Zhou Mon, Wed, Fri 9:25-10:15 Loria 250
Linear Models 312a/612a Joseph Chang Mon, Wed 11:35-12:50 17 Hillhouse Rm 05
Data analysis 361a/661a Lisha Chen Mon, Wed 2:30-3:45 DL 220
Statistical Inference 610a Andrew Barron Tues, Thurs 10:30-11:45 24 Hillhouse Rm 107
Statistical Case Studies 625a John Emerson Tues, Thurs 9:00-10:15 DL 120
Stochastic Processes and Bayesian Nonparametrics 655a Mokshay Madiman Tuesday 4:00-6:30 24 Hillhouse Rm 107
Statistical Computing 662a John Emerson Tues, Thurs 2:30-3:45 17 Hillhouse Rm 111
Information and Probability 668a Mokshay Madiman Thursday 4:00-6:30 24 Hillhouse Rm 107
Individual Studies 480ab Staff - -
Statistical Consulting 627ab John Emerson Friday 2:30-4:30 24 Hillhouse Rm 107
Independent Study 690ab Staff - -
Research Seminar in Probability 699ab Sekhar Tatikonda and David Pollard Friday 12:00-2:00 24 Hillhouse Rm 107
Departmental Seminar 700ab - Monday 4:15-5:30 24 Hillhouse Rm 107
Introductory Statistics 100b/500b Lisha Chen Mon, Wed, Fri 10:30-11:20 ML 211
Introductory Data Analysis 230b/530b/PLSC 530b John Emerson Mon, Wed 2:30-3:45 17 Hillhouse Rm 101
Theory of Statistics 242b/542b Andrew Barron Mon, Wed, Fri 9:25-10:15 ML 211
Stochastic Processes 251b/551b David Pollard Mon, Wed 1:00-2:15 WLH 119
Advanced Probability 330b/600b/MATH 330b David Pollard Tues, Thurs 2:30-3:45 24 Hillhouse Rm 107
Multivariate Statistics for Social Sciences 363b/660b Jonathan Reuning-Scherer Tues, Thurs 1:00-2:15 KRN 301
Data Mining and Machine Learning 365b/665b Lisha Chen Mon, Wed 2:30-3:45 ML 211
Senior Seminar and Project 490b Andrew Barron Monday 6:00-8:00 24 Hillhouse Rm 107
Decision Theory 611b Harrison Zhou Fri 9:00-11:30 24 Hillhouse Rm 107
Practical Work 626b Staff - -
Applied Spatial Statistics 674b/F&ES 781b Timothy Gregoire and Jonathan Reuning-Scherer Tues, Thurs 10:30-11:50 KRN 319
Asymptotics 618a - not taught this year-
Probabilistic Networks, Algorithms, and Applications 667a - not taught this year-
Nonparametric Statistics 680a - not taught this year-
Information theory 364b/664b - not taught this year-
Empirical Processes 609b - not taught this year-
Experimental Design 613b - not taught this year-
Statistical Methods in Genetics and Bioinformatics 645b - not taught this year-

Introductory Statistics (STAT 100b/STAT 500b)
Instructor: Lisha Chen
Time: Mon, Wed, Fri 10:30-11:20
Place: ML 211
An introduction to statistical reasoning. Topics include numerical and graphical summaries of data, data acquisition and experimental design, probability, hypothesis testing, confidence intervals, correlation and regression. Application of statistical concepts to data; analysis of real-world problems.
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Introduction to Statistics (STAT 101a-106a/STAT 501a-506a)
Instructor: Jonathan Reuning-Scherer and Staff
Time: Tues, Thurs 1:00-2:15
Place: OML 202
Webpage:  http://www.stat.yale.edu/Courses/QR/stat101106.html
A basic introduction to statistics, including numerical and graphical summaries of data, probability, hypothesis testing, confidence intervals, and regression. Each course focuses on applications to a particular field of study and is taught jointly by two instructors, one specializing in statistics and the other in the relevant area of application. The first seven weeks of classes are attended by all students in STAT 101-106 together, as general concepts and methods of statistics are developed. The remaining weeks are divided into field-specific sections that develop the concepts with examples and applications. Computers are used for data analysis. These courses are alternatives; they do not form a sequence and only one may be taken for credit. No prerequisites beyond high school algebra. May not be taken after STAT 100 or 109.

Students enrolled in STAT 101-106 who wish to change to STAT 109, or those enrolled in STAT 109 who wish to change to STAT 101-106, must submit a course change notice, signed by the instructor, to their residential college dean by Friday, September 28. The approval of the Committee on Honors and Academic Standing is not required.
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Introduction to Statistics: Life Sciences (STAT 101a/E&EB 210aG/MCDB 215a)
Instructor: Jonathan Reuning-Scherer and Walter Jetz
Time: Tues, Thurs 1:00-2:15
Place: OML 202
Statistical and probabilistic analysis of biological problems presented with a unified foundation in basic statistical theory. Problems are drawn from genetics, ecology, epidemiology, and bioinformatics.
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Introduction to Statistics: Political Science (STAT 102a/EP&E 203a/PLSC 425a)
Instructor: Jonathan Reuning-Scherer and Alan Gerber
Time: Tues, Thurs 1:00-2:15
Place: OML 202
Statistical analysis of politics and quantitative assessments of public policies. Problems presented with reference to a wide array of examples: public opinion, campaign finance, racially motivated crime, and health policy.
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Introduction to Statistics: Social Sciences (STAT 103a/SOCY 119a)
Instructor: Jonathan Reuning-Scherer
Time: Tues, Thurs 1:00-2:15
Place: OML 202
Descriptive and inferential statistics applied to analysis of data from the social sciences. Introduction of concepts and skills for understanding and conducting quantitative research.
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Introduction to Statistics: Medicine (STAT 105a)
Instructor: Jonathan Reuning-Scherer and David Salsburg
Time: Tues, Thurs 1:00-2:15
Place: OML 202
Statistical methods used in medicine and medical research. Practice in reading medical literature competently and critically, as well as practical experience performing statistical analysis of medical data.
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[ Introduction to Statistics: Data Analysis (STAT 106a) ]

Introduction to Statistics: Fundamentals (STAT 109a)
Instructor: Jonathan Reuning-Scherer
Time: Tues, Thurs 1:00-2:15
Place: OML 202
General concepts and methods in statistics. Meets for the first half of the term only. May not be taken after STAT 100 or 101-106.
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Introductory Data Analysis (STAT 230b/STAT 530b/PLSC 530b)
Instructor: John Emerson
Time: Mon, Wed 2:30-3:45
Place: 17 Hillhouse Rm 101
Survey of statistical methods: plots, transformations, regression, analysis of variance, clustering, principal components, contingency tables, and time series analysis. The R computing language and Web data sources are used.
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Probability and Statistics (STAT 238a/STAT 538a)
Instructor: Joseph Chang
Time: Tues, Thurs 1:00-2:15
Place: ML 211
Fundamental principles and techniques of probabilistic thinking, statistical modeling, and data analysis. Essentials of probability, including conditional probability, random variables, distributions, law of large numbers, central limit theorem, and Markov chains. Statistical inference with emphasis on the Bayesian approach: parameter estimation, likelihood, prior and posterior distributions, Bayesian inference using Markov chain Monte Carlo. Introduction to regression and linear models. Computers are used for calculations, simulations, and analysis of data.

After MATH 118 or 120. Some acquaintance with matrix algebra and computing assumed.
Extra: STAT 238 Extra Session,  Tues 6:30-8:00,  24 Hillhouse Rm 107
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Probability Theory with Applications (STAT 241a/STAT 541a/MATH 241a)
Instructor: Harrison Zhou
Time: Mon, Wed, Fri 9:25-10:15
Place: Loria 250
Introduction to probability theory. Topics include probability spaces, random variables, expectations and probabilities, conditional probability, independence, discrete and continuous distributions, central limit theorem, Markov chains, and probabilistic modeling.
Extra: STAT 241 TA Session,  Thurs 6:30-7:30,  24 Hillhouse Rm 107
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Theory of Statistics (STAT 242b/542b)
Instructor: Andrew Barron
Time: Mon, Wed, Fri 9:25-10:15
Place: ML 211
Study of the principles of statistical analysis. Topics include maximum likelihood, sampling distributions, estimation, confidence intervals, tests of significance, regression, analysis of variance, and the method of least squares. Some statistical computing.
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Stochastic Processes (STAT 251b/STAT 551b)
Instructor: David Pollard
Time:  Mon, Wed 1:00-2:15
Place: WLH 119
Introduction to the study of random processes, including Markov chains, Markov random fields, martingales, random walks, Brownian motion, and diffusions. Techniques in probability, such as coupling and large deviations. Applications chosen from image reconstruction, Bayesian statistics, finance, probabilistic analysis of algorithms, and genetics and evolution.
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Linear Models (STAT 312a/STAT 612a)
Instructor: Joseph Chang
Time: Mon, Wed 11:35-12:50
Place: 17 Hillhouse Rm 05
The geometry of least squares; distribution theory for normal errors; regression, analysis of variance, and designed experiments; numerical algorithms, with particular reference to the R statistical language.

After STAT 242 and MATH 222 or 225.

No final exam.
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Advanced Probability (STAT 330b/STAT 600b/MATH 330b)
Instructor: David Pollard
Time: Tues, Thurs 2:30-3:45
Place: 24 Hillhouse Rm 107
Measure theoretic probability, conditioning, laws of large numbers, convergence in distribution, characteristic functions, central limit theorems, martingales. Some knowledge of real analysis is assumed.
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Data analysis (STAT 361a/STAT 661a)
Instructor: Lisha Chen
Time: Mon, Wed 2:30-3:45
Place: DL 220
Selected topics in statistics explored through analysis of data sets using the R statistical computing language. Topics include linear and nonlinear models, maximum likelihood, resampling methods, curve estimation, model selection, classification, and clustering. Weekly sessions in the Statistical Computing laboratory.

After STAT 242 and MATH 222 or 225, or equivalents.
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Multivariate Statistics for Social Sciences (STAT 363b/STAT 660b)
Instructor: Jonathan Reuning-Scherer
Time: Tues, Thurs 1:00-2:15
Place: KRN 301
Introduction to the analysis of multivariate data as applied to examples from the social sciences. Topics include principal components analysis, factor analysis, cluster analysis (hierarchical clustering, k-means), discriminant analysis, multidimensional scaling, and structural equations modeling. Extensive computer work using either SAS or SPSS programming software.

Prerequisites: knowledge of basic inferential procedures and experience with linear models.
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[ Information theory (STAT 364b/STAT 664b) ]

Data Mining and Machine Learning (STAT 365b/STAT 665b)
Instructor: Lisha Chen
Time: Mon, Wed 2:30-3:45
Place: ML 211
Techniques for data mining and machine learning are covered from both a statistical and a computational perspective, including support vector machines, bagging, boosting, neural networks, and other nonlinear and nonparametric regression methods. The course will give the basic ideas and intuition behind these methods, a more formal understanding of how and why they work, and opportunities to experiment with machine learning algorithms and apply them to data. After STAT 242b.
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Individual Studies (STAT 480ab)
Instructor: Staff
Time: -
Place: -
Directed individual study for qualified students who wish to investigate an area of statistics not covered in regular courses. A student must be sponsored by a faculty member who sets the requirements and meets regularly with the student. Enrollment requires a written plan of study approved by the faculty adviser and the director of undergraduate studies.

Permission required. No final Exam.
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Senior Seminar and Project (STAT 490b)
Instructor: Andrew Barron
Time: Monday 6:00-8:00
Place: 24 Hillhouse Rm 107
Under the supervision of a member of the faculty, each student works on an independent project. Students participate in seminar meetings at which they speak on the progress of their projects.

Permission required. No final Exam.
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[ Empirical Processes (STAT 609b) ]

Statistical Inference (STAT 610a)
Instructor: Andrew Barron
Time: Tues, Thurs 10:30-11:45
Place: 24 Hillhouse Rm 107
A systematic development of the mathematical theory of statistical inference covering methods of estimation, hypothesis testing, and confidence intervals. An introduction to statistical decision theory. Undergraduate probability at the level of Statistics 241a assumed.
Extra: STAT 610 Extra Session,  Fri 10:30-11:45,  24 Hillhouse Rm 107
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Decision Theory (STAT 611b)
Instructor: Harrison Zhou
Time: Fri 9:00-11:30
Place: 24 Hillhouse Rm 107
A detailed study of some topics in statistical decision theory, including: admissibility and minimaxity; the James-Stein estimator; Stein's unbiased estimator of risk; empirical Bayes estimators; hierarchical Bayes methods and random effects; complete class theorems; asymptotic minimaxity for nonparametric estimation; sparsity models. Prerequisite: Statistics 610.
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[ Experimental Design (STAT 613b) ]

[ Asymptotics (STAT 618a) ]

Statistical Case Studies (STAT 625a)
Instructor: John Emerson
Time: Tues, Thurs 9:00-10:15
Place: DL 120
Webpage:  https://classesv2.yale.edu/
Statistical analysis of a variety of statistical problems using real data. Emphasis on methods of choosing data, acquiring data, assessing data quality, and the issues posed by extremely large data sets. Extensive computations using R.
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Practical Work (STAT 626b)
Instructor: Staff
Time: -
Place: -
Individual one-semester projects, with students working on studies outside the Department, under the guidance of a statistician. This course is a one-credit requirement for the Ph.D. degree.
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Statistical Consulting (STAT 627ab)
Instructor: John Emerson
Time: Friday 2:30-4:30
Place: 24 Hillhouse Rm 107
Webpage:  http://www.stat.yale.edu/~jay/627.html
Statistical consulting and collaborative research projects often require statisticians to explore new topics outside their area of expertise. This course exposes students to real problems, requiring them to draw on their expertise in probability, statistics, and data analysis. Students complete the course with individual projects supervised jointly by faculty outside the department and by one of the instructors. Students enroll for both terms and receive one credit at the end of the year.
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[ Statistical Methods in Genetics and Bioinformatics (STAT 645b) ]

Stochastic Processes and Bayesian Nonparametrics (STAT 655a)
Instructor: Mokshay Madiman
Time: Tuesday 4:00-6:30
Place: 24 Hillhouse Rm 107
An exposition of the modern Bayesian approach to nonparametric theory. Includes an introduction to the relevant stochastic process theory. Assumes a knowledge of probability and statistics at the level of STAT 600 and 610.
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Statistical Computing (STAT 662a)
Instructor: John Emerson
Time: Tues, Thurs 2:30-3:45
Place: 17 Hillhouse Rm 111
Topics in the practice of data analysis and statistical computing, with particular attention to problems involving massive data sets or large, complex simulations and computations. Progamming with R, C/C++, and Perl, memory management, interactive and dynamic graphics, and parallel computing.
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[ Probabilistic Networks, Algorithms, and Applications (STAT 667a) ]

Information and Probability (STAT 668a)
Instructor: Mokshay Madiman
Time: Thursday 4:00-6:30
Place: 24 Hillhouse Rm 107
Study of basic notions of information theory and the role they play not just in data compression and communication, but also in other areas of probability, statistics and combinatorics. Topics include the applications of entropy inequalities to combinatorics and functional analysis, information-theoretic understanding of probabilistic limit theorems, the asymptotic equipartition property, Fisher information, large deviations, hypothesis testing, and other topics to be decided. Connections to physics, electrical engineering, and various parts of mathematics will be highlighted as appropriate. No prior exposure to information theory is required, but it will be assumed that students are fluent and not just passable with probability at the level of STAT 541, and also that they have some mathematical maturity.
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Applied Spatial Statistics (STAT 674b/F&ES 781b)
Instructor: Timothy Gregoire and Jonathan Reuning-Scherer
Time: Tues, Thurs 10:30-11:50
Place: KRN 319
An introduction to spatial statistical techniques with computer applications. Topics include spatial sampling, visualizing spatial data, quantifying spatial association and autocorrelation, interpolation methods, fitting variograms, kriging, and related modeling techniques for spatially correlated data. Examples are drawn from ecology, sociology, public health, and subjects proposed by students. Four to five lab/homework assignments and a final project. The class makes extensive use of the R programming language as well as ArcGIS.
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[ Nonparametric Statistics (STAT 680a) ]

Independent Study (STAT 690ab)
Instructor: Staff
Time: -
Place: -
By arrangement with faculty. Approval of Director of Graduate Studies required.
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Research Seminar in Probability (STAT 699ab)
Instructor: Sekhar Tatikonda and David Pollard
Time: Friday 12:00-2:00
Place: 24 Hillhouse Rm 107
Webpage:  http://www.stat.yale.edu/~ypng
Continuation of the Yale Probability Group Seminar. Student and faculty explanations of current research in areas such as random graph theory, spectral graph theory, Markov chains on graphs, and the objective method.

Credit only with the explicit permission of the seminar organizers.
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Departmental Seminar (STAT 700ab)
Instructor: -
Time: Monday 4:15-5:30
Place: 24 Hillhouse Rm 107
Webpage:  http://www.stat.yale.edu/Seminars/2011-12/
Important activity for all members of the department. See webpage for weekly seminar announcements.
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