Statistics Department
Course List for Fall 2016/Spring 2017

• Organizational Meeting for all FALL term courses whose times are not listed below: TBD.
• Those interested in attending one of the courses but unable to be present at this meeting should inform Jay Emerson beforehand and submit their schedules.
• Courses numbered 600 or above (such as Stat 610a) are intended primarily for graduate students. If such a course does not have an undergraduate cross-listing, undergraduates need special permission to enroll.
• Tables of class times: FALL and SPRING
• Course lists for other years:
     1994-95    1995-96    1996-97    1997-98    1998-99    1999-00    2000-01    2001-02    2002-03    2003-04    2004-05    2005-06    2006-07    2007-08    2008-09    2009-10    2010-11    2011-12    2012-13    2013-14    2014-15    2015-16    [this year]    2017-18    2018-19    2019-20    2020-21    2021-22    2022-23    2023-24    2024-25    2025-26    2026-27 .
  These pages are not being updated; some of the links may no longer be working.
• Director of Undergraduate Studies: Joseph Chang
• Director of Graduate Studies: Jay Emerson and Andrew Barron
• Course requirements for Ph.D. and M.A. students.

Introductory Statistics (STAT 100b/STAT 500b)

Instructor: Jessi Cisewski
Time: Mon, Wed, Fri 10:30-11:20
Place: DL 220
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: 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 (1/2 credit) (STAT 109a)

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
This is a 1/2 credit option for completing the first part of the big STAT 103-106 course (see above). If you would like to take STAT 230 but never had any prior introductory statistics, you should consider this course.

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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 230a/STAT 530a/PLSC 530a)

Instructor: John Emerson
Time: Mon, Wed 1:00-2:15 + 1 HTBA required section
Place: TBA
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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Introductory Data Analysis (STAT 230b/STAT 530b/PLSC 530b)

Instructor: Susan Wang and Joe Chang
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 and Susan Wang
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: Yihong Wu
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: Sahand Negahban
Time:  Mon, Wed 1:00-2:15
Place: WLH 208
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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Computational Tools for Data Science (STAT 262a)

Instructor: Dan Spielman and Sahand Negahban
Time: Tues, Thurs 2:30-3:45
Place: TBA
Assumes math chops and some type of programming.

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[ Applied Linear Models (STAT 531a) ]

Linear Models (STAT 312a/STAT 612a)

Instructor: David Pollard
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 and Susan Wang
Time: Tues, Thurs 9:00-10:15 + 1 HTBA (tentatively 2 versions)
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. Two versions of this may be offered experimentally in 2016-17.

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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.spring2017/
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 361b/STAT 661b)

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 or concurrently with 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)

Instructor: Yihong Wu
Time: Tues, Thurs 11:35-12:50
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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Applied Data Mining and Machine Learning (STAT 365b/STAT 665b)

Instructor: Susan Wang
Time: Mon, Wed 11:35-12:50
Place: SCL 160
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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Statistical Learning Theory (STAT 669b)

Instructor: Sahand Negahban
Time: Mon, Wed, 2:30-3:45
Place: 24 HH Room 107
Introduction to theoretical analysis of machine learning algorithms. Focus on the statistical and computational aspects. Will cover subjects such as decision theory, empirical process theory, and convex optimization. Prerequisites linear algebra, multivariable calculus, stochastic processes, and introduction to machine learning such as Stat 365b or a similar course.

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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: TBA
Place: 24 Hillhouse Room 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: 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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Selected Topics in Statistical Decision Theory (STAT 611b)

Instructor: Harrison Zhou
Time: Mon 10:00-12:30
Place: 24 Hillhouse Rm 107
In this course we will review some recent developments in statistical decision theory including nonparametric estimation, high dimensional (non)linear estimation, low rank and sparse matrices estimation, covariance matrices estimation, graphical models, and network analysis.

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[ Experimental Design (STAT 613b) ]

[ Asymptotics (STAT 618b) ]

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)

Instructor: Hongyu Zhao
Time: Thurs 1:00-2:50
Place: LEPH 102
Introduction to problems, algorithms, and data analysis approaches in computational biology and bioinformatics; stochastic modeling and statistical methods applied to problems such as mapping disease-associated genes, analyzing gene expression microarray data, sequence alignment, and SNP analysis. Statistical methods include maximum likelihood, EM, Bayesian inference, Markov chain Monte Carlo, and some methods of classification and clustering; models include hidden Markov models, Bayesian networks, and the coalescent. The limitations of current models, and the future opportunities for model building, are critically addressed. Prerequisite: STAT 661a, 538a, or 542b. Prior knowledge of biology is not required, but some interest in the subject and a willingness to carry out calculations using R is assumed.

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Topics in Bayesian Inference and Data Analysis (STAT 654b)

Instructor: Joe Chang
Time: Wed, Thurs 4:00-5:15
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 662b)

Instructor: John Emerson
Time: Tues, Thurs 9:00-10:15
Place: 17 HLH, 101 (TEAL)
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/Python, computational efficiency, memory management, interactive and dynamic graphics, and parallel computing.

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

Instructor: Sekhar Tatikonda
Time: Tues, Thurs 1:00-2:15
Place: 24 Hillhouse Rm 107
Webpage:  http://pantheon.yale.edu/~sct29/courses.dir/prob_networks2009.html
This course examines probabilistic and computational methods for the statistics modeling of complex data. The emphasis is on the unifying framework provided by graphical models, a formalism that merges aspects of graph theory and probability theory. Graphical models: Markov random fields, Bayesian networks, and factor graphs. Algorithms: filtering, smoothing, belief-propagation, sum-product, and juntion tree. Variational techniques: mean-field and convex relaxtions. Markov processes on graphics: MCMC, factored HMMs, and Glauber dynamics. Some statistical physics techniques: cavity and replica methods. Applications to error-correcting codes, computer vision, bio-informatics, and combinatorial optimization.

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Applied Spatial Statistics (STAT 674b/F&ES 781b)

Instructor: Jonathan Reuning-Scherer
Time: Tues, Thurs 10:30-11:50
Place: BOWERS
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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Topological Data Analysis (STAT 675a)

Instructor: Jessi Cisewski
Time: Mon 1:00-3:45
Place: 24 Hillhouse Rm 107

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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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Design and Analysis of Algorithms (CPSC 365)

Instructor: Daniel Spielman
Time: Tues, Thurs 2:30-3:45
Place: DL 220
Paradigms for problem solving: divide and conquer, recursion, greedy algorithms, dynamic programming, randomized and probabilistic algorithms. Techniques for analyzing the efficiency of algorithms and designing efficient algorithms and data structures. Algorithms for graph theoretic problems, network flows, and numerical linear algebra. Provides algorithmic background essential to further study of computer science. After CPSC 202 and 223.

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Optimization Techniques (ENAS 530)

Instructor: Sekhar Tatikonda
Time: Tues, Thurs 1.00-2.15
Place: WLH 117
Fundamental theory and algorithms of optimization, emphasizing convex optimization. The geometry of convex sets, basic convex analysis, the principle of optimality, duality. Numerical algorithms: steepest descent, Newton's method, interior point methods, dynamic programming, unimodal search. Applications from engineering and the sciences.

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