Statistics Department
Course List for Fall 2015/Spring 2016

Revised: 30 November 2015
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 SSS 114
Probability and Statistics 238a/538a Joe 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 WLH 208
Linear Models 312a/612a Taylor Arnold Mon, Wed 11:35-12:50 WTS A60
Statistical Case Studies 325a/625a John Emerson Tues, Thurs 9:00-10:15 + 1 HTBA 17 Hillhouse, TEAL
Data Analysis 361a/661a Jessi Cisewski Mon, Wed 2:30-3:45 ML 211
Statistical Inference 610a Harrison Zhou Tues, Thurs 10:30-11:45 24 Hillhouse Rm 107
Topics in Bayesian Inference and Data Analysis 654a Joe Chang Wed 4:00-6:30 (tentatively) 24 Hillhouse Rm 107
High-Dimensional Function Estimation 682a Andrew Barron Mon, Wed 9:00-10:15 24 Hillhouse Room 107
Individual Studies 480ab Staff - -
Statistical Consulting 627ab John Emerson Fri 2:30-4:30 24 Hillhouse Rm 107
Independent Study or Topics Course 690ab DGS - -
Research Seminar in Probability 699ab Sekhar Tatikonda and David Pollard Fri 11:00-1:00 24 Hillhouse Rm 107
Departmental Seminar 700ab - Mon 4:15-5:30 24 Hillhouse Rm 107
Introductory Statistics 100b/500b Jessi Cisewski Mon, Wed, Fri 10:30-11:20 ML 211
Introductory Data Analysis 230b/530b/PLSC 530b John Emerson Tues, Thurs 9:00-10:15 DL 220
Theory of Statistics 242b/542b Andrew Barron Mon, Wed, Fri 9:25-10:15 ML 211
Stochastic Processes 251b/551b Amin Karbasi Mon, Wed 1:00-2:15 WLH 116
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 Burke Auditorium / Kroon Hall
Information Theory 364b/664b Andrew Barron Tues, Thurs 9:00-10:15 24 Hillhouse Rm 107
Data Mining and Machine Learning 365b/665b Taylor Arnold Mon, Wed 2:30-3:45 DL 220
Asymptotics 618b David Pollard Tuesday 10:30-12:00 + 1 HTBA (tentative) 24 Hillhouse basement
Practical Work 626b John Emerson - -
Applied Spatial Statistics 674b/F&ES 781b Timothy Gregoire and Jonathan Reuning-Scherer Tues, Thurs 10:30-11:50 Marsh Hall Rotunda, 360 Prospect St
Statistical Computing 662a - not taught this year-
Probabilistic Networks, Algorithms, and Applications 667a - not taught this year-
Senior Seminar and Project 490b - not taught this year-
Empirical Processes 609b - not taught this year-
Decision Theory 611b - 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: Jessi Cisewski
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.
Extra: Stat 100 extra (tentative),  Wed 5:00-7:00,  24 Hillhouse Rm 107
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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: SSS 114
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 Kelly Rader
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
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: Tues, Thurs 9:00-10:15
Place: DL 220
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. After STAT 100 or the equivalent or with permission from the instructor.
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Probability and Statistics (STAT 238a/STAT 538a)
Instructor: Joe 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.

Prerequisite: knowledge of single variable calculus is assumed. Some brief acquaintance with multivariable calculus (e.g. double integrals) and matrices would also be helpful but are not required.
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: WLH 208
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: Amin Karbasi
Time:  Mon, Wed 1:00-2:15
Place: WLH 116
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: Taylor Arnold
Time: Mon, Wed 11:35-12:50
Place: WTS A60
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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Statistical Case Studies (STAT 325a/625a)
Instructor: John Emerson
Time: Tues, Thurs 9:00-10:15 + 1 HTBA
Place: 17 Hillhouse, TEAL
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. Limited size, with permission from the instructor required.
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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
Webpage:  http://www.stat.yale.edu/~pollard/Courses/600.spring2016/
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: Jessi Cisewski
Time: Mon, Wed 2:30-3:45
Place: ML 211
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.

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: Burke Auditorium / Kroon Hall
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)
Instructor: Andrew Barron
Time: Tues, Thurs 9:00-10:15
Place: 24 Hillhouse Rm 107
Foundations of information theory in mathematical communications, statistical inference, statistical mechanics, probability, and algorithmic complexity. Quantities of information and their properties: entropy, conditional entropy, divergence, redundancy, mutual information, channel capacity. Basic theorems of data compression, data summarization, and channel coding. Applications in statistics and finance. After Statistics 241.
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Data Mining and Machine Learning (STAT 365b/STAT 665b)
Instructor: Taylor Arnold
Time: Mon, Wed 2:30-3:45
Place: DL 220
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) ]

[ Empirical Processes (STAT 609b) ]

Statistical Inference (STAT 610a)
Instructor: Harrison Zhou
Time: Tues, Thurs 10:30-11:45
Place: 24 Hillhouse Rm 107
Webpage:  http://www.stat.yale.edu/~pollard/Courses/610.fall2014/
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) ]

[ Experimental Design (STAT 613b) ]

Asymptotics (STAT 618b)
Instructor: David Pollard
Time: Tuesday 10:30-12:00 + 1 HTBA (tentative)
Place: 24 Hillhouse basement
Webpage:  http://www.stat.yale.edu/~pollard/Courses/618.spring2016/
A careful study of some standard asymptotic techniques in statistics and econometrics, and their modern refinements. Topics selected from classical likelihood theory and M-estimation; empirical process methods; concentration inequalities; semiparametric models; local asymptotic normality; concepts of efficiency. Students will be expected to read research papers and make presentations in class. Prerequisites: knowledge of probability at the level of STAT 600b.
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Practical Work (STAT 626b)
Instructor: John Emerson
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: Fri 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) ]

Topics in Bayesian Inference and Data Analysis (STAT 654a)
Instructor: Joe Chang
Time: Wed 4:00-6:30 (tentatively)
Place: 24 Hillhouse Rm 107
Topics in the theory and practice of Bayesian statistical inference, ranging from a review of fundamentals to questions of current research interest. Motivation for the Bayesian approach, Bayesian computation, Monte Carlo methods, use of software (including R, BUGS, and possibly others), asymptotics, model checking and comparison, empirical Bayes approaches, hierarchical models, and Bayesian nonparametrics. A selection of other topics as time permits; possibilities include Bayesian design, variational methods, and approximate Bayesian computation. Assumed background includes probability and statistics at least at the level of STAT 541 and 542, Markov Chains as covered in STAT 551, and computing in R.
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[ Statistical Computing (STAT 662a) ]

[ Probabilistic Networks, Algorithms, and Applications (STAT 667a) ]

Applied Spatial Statistics (STAT 674b/F&ES 781b)
Instructor: Timothy Gregoire and Jonathan Reuning-Scherer
Time: Tues, Thurs 10:30-11:50
Place: Marsh Hall Rotunda, 360 Prospect St
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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Independent Study or Topics Course (STAT 690ab)
Instructor: DGS
Time: -
Place: -
By arrangement with faculty. Approval of Director of Graduate Studies required.
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High-Dimensional Function Estimation (STAT 682a)
Instructor: Andrew Barron
Time: Mon, Wed 9:00-10:15
Place: 24 Hillhouse Room 107
Modern developments of high-dimensional function estimation, building from classical one-dimensional ingredients. Theory and methods for approximation, estimation, and computation. The blessing and the curse of high-dimensionality. Piece-wise polynomial, sinusoidal, and sigmoidal (artificial neural network) models. Product and ridge-basis models. Selection criteria. Deterministic and stochastic optimization strategies, including gradient methods, greedy algorithms, annealing and the associated theory of evolution of the parameters of the function estimates. Students will be responsible for a literature-based theory project/presentation and a computational project/presentation.
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Research Seminar in Probability (STAT 699ab)
Instructor: Sekhar Tatikonda and David Pollard
Time: Fri 11:00-1:00
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: Mon 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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