Course List for 2004-2005

• Organizational Meeting for all Spring term courses whose times are not listed below: Friday, January 7th, 10:00am, Room 107, 24 Hillhouse Avenue. Those interested in attending one of the courses but unable to be present at this meeting should inform Kathryn Young beforehand and submit their schedules.
• Courses whose numbers end with a are taught in the FALL; courses whose numbers end with b are taught in the SPRING.
• Schedule of times when 24 Hillhouse Room 107 is in use.
  

course	number	instructor	level	time
Introduction to Statistics	101-106a	Reuning-Scherer & Staff	intro, no prereqs	T,Th 1:00-2:15
Probability and Statistics for Scientists	238a	Chang	intro	M,W,F 2:30-3:20
Probability Theory with Applications	241/541a	Leeb	intro, with calculus	M,W,F 9:30-10:20
Linear Models	312/612a	Leeb	intermediate	T,Th 9:00-10:15
Data Analysis	361/661a	Emerson	intermediate	M, W 2:30-3:45
Stochastic Calculus	603a	Pollard		please see course listing below
Foundations of Statistics	605a	Hartigan		M,W 1:00-2:15
Statistical Inference	610a	Zhou	intro grad	T,Th 10:30-11:45
Statistical Case Studies	625a	Emerson/Hartigan	intermediate grad	T,Th 2:30-3:45
Internship in Statistical Research	695a	Hartigan		N/A
Introductory Statistics	100b	Emerson	intro	M,W,F 10:30 - 11:20
Introductory Data Analysis	230/530b	Hartigan	intro	M,W  2:30-3:45
Theory of Statistics	242/542b	Zhou/Barron	intro, with calculus	M,W,F 9:30-10:20
Stochastic Processes	251/551b	Chang	intermediate	M,W 1:00-2:15
Advanced Probability	330/600b	Pollard	adv. undergrad/  intermediate grad	T,Th 2:30-3:45
Information Theory	364/664b	Yeh	intermediate	T,Th 9:00-10:15
Data Mining and Machine Learning	365/665b	Leeb		M,W 11:30-12:45
Applied Math Senior Seminar	AM490b	Barron		W 2:30 - 4:20
Inequalities for Probability & Statistics	607b	Pollard		M, W 1:00 - 2:15
Practical Work	626b	Emerson/Hartigan	adv. grad	contact instructor
Statistical Consulting	627b	Emerson/Hartigan		T 2:30 - 3:45, F 1:00 - 4:00
Statistical Methods in Genetics and Bioinformatics	645b	Chang		T, TH 10:30 - 11:20, F 2:00 - 2:50
Multivariate Methods for the Social Sciences	660b	Reuning-Scherer		T, TH 1:00 - 2:15
Probabilistic Networks, Algorithms, and Applications	667b	Tatikonda		T, TH 11:30 - 12:45

Primarily undergraduate courses

STAT 101a / E&EB 210aG / MCDB 215a, Introduction to Statistics: Life Sciences.
Instructor:  Mr. Jonathan Reuning-Scherer / Mr. Gunter Wagner
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.

STAT 102a / EP&E 203a / PLSC 425a, Introduction to Statistics: Political Science.
Instructor:  Mr. Jonathan Reuning-Scherer / Mr. Donald Green
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.

STAT 103a / SOCY 119a, Introduction to Statistics: Social Sciences.
Instructor:  Mr. Jonathan Reuning-Scherer
An introduction to probability and statistics, with emphasis on experimental design and data analysis.  Survey of many of the great experiments in social science.  Topics include obedience to authority, conformity to social pressure, and susceptibility to perceptual distortions.

[STAT 104a / PSYC 201a, Introduction to Statistics: Psychology.  Not offered this semester.]

STAT 105a, Introduction to Statistics:  Medicine.
Instructor:  Mr. Jonathan Reuning-Scherer / Mr. David Salsburg
Statistical methods relied upon in medicine and medical research.  Practice in reading medical literature competently and critically, as well as practical experience performing statistical analysis of medical data.

[STAT 106a, Introduction to Statistics: Data Analysis.  Not offered this semester.]

STAT 230b, Introductory Data Analysis (SPRING)

Cross-listing: Statistics 530a, PLSC 530b
Instructor: Mr. John Hartigan
Time: 2:30 - 3:45
Place: PR 140 Statlab
Survey of statistical methods: plots, transformations, regression, analysis of variance, clustering, principal components, contingency tables, and time series analysis. Uses SPLUS and Web data sources. After or concurrent with Statistics 101-105.

STAT 238a, Probability and Statistics for Scientists (FALL)
Cross-listing: Statistics 538a
Instructor: Mr. Joseph Chang
Time: Mon, Wed, Fri 2:30-3:20
Place:   ML 104
Fundamental principles and techniques that help scientists think probabilistically, develop statistical models, and analyze data. Essentials of probability: conditional probability, random variables, distributions, law of large numbers, central limit theorem, 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 throughout for calculations, simulations, and analysis of data.  After MATH 118a or b or 120a or b.  Some acquaintance with matrix algebra and computing assumed.

STAT 241a, Probability Theory with Applications (FALL)
Cross-listing: Statistics 541a
Instructor: Mr. Hannes Leeb
Time: Mon, Wed, Fri 9:30 - 10:20
Place:  WLH 208
A first course in probability theory: probability spaces, random variables, expectations and probabilities, conditional probability, independence, some discrete and continuous distributions, central limit theorem, law of large numbers. After or concurrent with Mathematics 120a or b or equivalents.

STAT 242b, Theory of Statistics (SPRING)
Cross-listing: Statistics 542b, Mathematics 242b
Instructor: Mr. Harrison Zhou / Mr. Andrew Barron
Time: Mon, Wed, Fri 9:30 - 10:20
Place:  BCT 102
Principles of statistical analysis: maximum likelihood, sampling distributions, estimation, confidence intervals, tests of significance, regression, analysis of variance, and the method of least squares. After Statistics 241a; after or concurrent with Mathematics 222.
[MORE COURSE INFORMATION]

STAT 251b, Stochastic Processes (SPRING)
Cross-listing: Statistics 551b
Instructor: Mr. Joseph Chang
Time: Mon, Wed 1 - 2:15
Place:  ML 211
Introduction to the study of random processes, including Markov chains, Markov random fields, martingales, random walks, Brownian motion and diffusions. Tecniques in probability, such as coupling and large deviations. Applications to image reconstruction, Bayesian statistics, finance, probabilistic analysis of algorithms, genetics and evolution. After Statistics 241a or equivalent.

STAT 312a, Linear Models (FALL)
Cross-listing: Statistics 612a
Instructor: Mr. Hannes Leeb
Time: Tues, Thurs 9:00-10:15
Place:   24 Hillhouse, Room 107
The geometry of least squares; distribution theory for normal errors; regression, analysis of variance, and designed experiments; numerical algorithms (with particular reference to Splus); alternatives to least squares. Generalized linear models. Linear algebra and some acquaintance with statistics assumed.

STAT 361a, Data Analysis (FALL)
Cross-listing: Statistics 661a
Instructor: Mr. John Emerson
Time: Mon, Wed 2:30 - 3:45
Place: Statlab - 140 Prospect
Through analysis of data sets using the Splus statistical computing language, study of a selection of statistical topics such as linear and nonlinear models, maximum likelihood, resampling methods, curve estimation, model selection, classification and clustering. After Statistics 242 and Mathematics 222b or 225a or b, or equivalents.

STAT 364b, Information Theory (SPRING)
Cross-listing: Statistics 664b
Instructor:  Mr. Edmund Yeh
Time:    Tue, Thu 9:00 - 10:15
Place: 24 Hillhouse Avenue, Room 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.

STAT 365b, Data Mining and Machine Learning (SPRING)
Cross-listing: Statistics 665b
Instructor:  Mr. Hannes Leeb
Time:    Mon, Wed 11:30 - 12:45
Place:  24 Hillhouse, Room 107
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.

AM490b, Applied Math Senior Seminar and Project (SPRING)
Cross-listing:
Instructor: Mr. Andrew Barron
Time:  Wed 2:30 - 3:20
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.  Some meetings are devoted to talks by visiting applied mathematicians.

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Primarily graduate courses

Director of Graduate Studies:  Professor John Hartigan.

STAT 600b, Advanced Probability (SPRING)
Cross-listing: Statistics 330b
Instructor: Mr. David Pollard
Time:  Tues, Thurs 2:30 - 3:45
Place:  WLH 117
Measure theoretic probability, conditioning, laws of large numbers, convergence in distribution, characteristic functions, central limit theorems, martingales. Some knowledge of real analysis is assumed.
[MORE COURSE INFORMATION]

STAT 603a, Stochastic Calculus (FALL)
Instructor:  Mr. David Pollard
Time:  Please see MORE COURSE INFORMATION
Place: 24 Hillhouse, Room 107
Martingales in discrete and continuous time, Brownian Motion, Sample path properties, predictable processes, stochastic integrals with respect to Brownian motion and semimartingales, stochastic differential equations. Applications mostly to counting processes and finance. Knowledge of measure-theoretic probability at the level of Statistics 600 is a prerequisite for the course,although some key concepts, such as conditioning, are reviewed. After Statistics 600.
[MORE COURSE INFORMATION]

STAT 605a, Foundations of Statistics (FALL)
Instructor:  Mr. John Hartigan
Time: Mon, Wed 1:00-2:15 pm
Place: 24 Hillhouse, Room 107
The course investigates philosophical and historical issues in the foundations of statistics. The origins and evolution of probability. The Bayesian-frequentist dichotomy. Is decision theory necessary or useful? Is robustness possible? Are asymptotic results applicable? How are independence assumptions justified, and what to do if they are not? Puzzles and paradoxes. The likelihood and invariance principles. Fiducial inference. Practical probability.

STAT 607b, Inequalities for Probability & Statistics (SPRING)
Instructor:  Mr. David Pollard
Time:  M, W 1:00 - 2:15 pm
Place: 24 Hillhouse, Room 107
A guided tour of some inequalities useful in statistical and probabilistic problems. The course will be broken into independent segments, each treating a specific method and an illustrative application. Acquaintance with probability at the 600 level helpful for some segments. Possible topics: convexity arguments; tail bounds for martingales and independent summands; metric entropy and maximal inequalities; VC dimension and combinatorial methods; distances between probability measures; majorizing measures and generic chaining;  isoperimetric inequalities; concentration inequalities; Gaussian processes. Applications to: statistical inference; asymptotic theory; minimax rates of convergence; machine learning; complexity.
[MORE COURSE INFORMATION]

STAT 610a, Statistical Inference (FALL)
Instructor: Mr. Harrison Zhou
Time:  Tues, Thurs 10:30-11:45 am
Place:  24 Hillhouse Avenue, Room 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.

STAT 625a, Statistical Case Studies (FALL)
Instructor: Mr. John Emerson and Mr. John Hartigan.
Time: Tues, Thurs 2:30-3:45 pm
Place:  124 Prospect St, Room B-13, Brewster Hall
Statistical analysis of a variety of problems including the value of a baseball player, the fairness of real estate taxes, how to win the Tour de France, energy consumption in Yale buildings, and interactive questionnaires for course evaluations.  We will emphasize methods of choosing data, acquiring data, and assessing data quality.  Computations will use R.
[MORE COURSE INFORMATION]

STAT 626b, Practical Work (SPRING)
Instructor: Mr. John Emerson / Mr. John Hartigan
Time: contact instructor
Place:  contact instructor
Individual one-semester projects, with students working on studies outside the Department, under the guidance of a statistician.

STAT 627b, Statistical Consulting (SPRING)
Instructor: Mr. John Emerson / Mr. John Hartigan
Time: Tues 2:30 - 3:45 pm, Fri 1:00 - 4:00 pm
Place:  124 Prospect Street, Room B-13, Brewster Hall
Statistical consulting and collaborative research projects usually require the statistician 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 consulting projects, under the guidance of a statistician.
The course will meet twice a week; classes and assignments will center around discussing and working on problems posed by researchers from across campus. Prior coursework in statistics is assumed: the equivalent of at least two courses in statistics or its applications, experience with methods of data analysis, or with permission from the instructor.

STAT 645b, Statistical Methods in Genetics and Bioinformatics (SPRING)
Instructor: Mr. Joseph Chang
Time: Tues, Thurs 10:30 - 11:20, Fri 2:00 - 2:50
Place: 24 Hillhouse Avenue, Room 107
Stochastic modeling and statistical methods applied to problems such as mapping quantitative trait loci, analyzing gene expression data, sequence alignment, and reconstructing evolutionary trees. Statistical methods include maximum likelihood, Bayesian inference, Monte Carlo Markov chains, and some methods of classification and clustering. Models introduced include variance components, hidden Markov models, Bayesian networks, and coalescent. Recommended background: Stat 541, Stat 542. Prior knowledge of biology is not required.

STAT 660b, Multivariate Statistics for Social Sciences (SPRING)
Instructor:  Mr. Jonathan Reuning-Scherer
Time:; Tues, Thur 1:00 pm - 2:15 pm
Place:  ML 104
A practical introduction to the analysis of multivariate data as applied to
examples from the social sciences. Topics to include multivariate analysis of variance (MANOVA),
principle components analysis, cluster analysis (hierarchical clustering, k-means), canonical correlation,
multidimensional scaling, factor analysis, discriminant analysis, and structural equations modeling.
Emphasis is placed on practical application of multivariate techniques to a variety of examples in the
social sciences. There are regular homework assignments and a final project. Regular use of some
statistical software package (students may choose among SAS, SPSS, and MINITAB). A complete
syllabus will be available on the classes server.

STAT 667b, Probabilistic Networks, Algorithms, and Applications (SPRING)
Cross-listing: ENAS 503
Instructor:  Mr. Sekhar Tatikonda
Time:  Tues, Thurs 11:30 - 12:45
Place:  AKW 500
This course examines probabilistic and computational methods for the statistical modeling of complex data. The emphasis will be 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 junction tree.  Variational techniques: mean-field and convex relaxations.  Markov processes on graphs: 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.
[MORE COURSE INFORMATION]

STAT 695a, Internship in Statistical Research (1 credit) (FALL)
Instructor: Mr. John Hartigan
The Internship is designed to give students an opportunity to gain practical exposure to problems in the analysis of statistical data, as part of a research group within industries such as: medical and pharmaceutical research, financial, information technologies, telecommunications, public policy, and others.  The Internship experience often serves as a basis for the Ph.D. dissertation.  Students will work with the Director of Graduate Studies and other faculty advisors to select suitable placements.

Students will submit a one-page description of their Internship plans to the DGS by May 1st, which will be evaluated by the DGS and other faculty advisors by May 15th.  Upon completion of the Internship, students shall submit a written report of their work to the DGS, no later than October 1st.  The Internship will be graded on a Satisfactory/ Unsatisfactory basis, and will be based on the student's written report and an oral presentation.
This course is an elective requirement for the Ph.D. degree.

STAT 699, Research Seminar in Statistics (SPRING)
Instructor:  Mr. Sekhar Tatikonda / Mr. David Pollard
Time: Friday, 10:00 - 12:00
Place: 24 Hillhouse Avenue, Room 107

STAT 700, Departmental Seminar
Time: Monday 4:15 pm - 5:30 pm
Place: 24 Hillhouse Avenue, Room 107
Important activity for all members of the department. See weekly seminar announcements.

Course lists for prior years may be found here. Please note these older pages are not being updated, and as such, some of the links may no longer be working.

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Revision: 22 July 2004