Statistics

Faculty of the Department of Statistics

Xiao-Li Meng, Whipple V.N. Jones Professor of Statistics (Chair)
Alan Agresti, Visiting Professor of Statistics (University of Florida)
Edoardo Maria Airoldi, Assistant Professor of Statistics
Jose Blanchet, Visiting Assistant Professor of Statistics (Columbia University)
Joseph K. Blitzstein, Assistant Professor of Statistics (Assistant Director of Undergraduate Studies)
Tirthankar Dasgupta, Assistant Professor of Statistics
S.C. Samuel Kou, Professor of Statistics (Director of Graduate Studies)
Yoonjung Lee, Assistant Professor of Statistics
Julia Yi-Hsin Lin, Lecturer on Statistics (Medical School)
Jun S. Liu, Professor of Statistics
Carl N. Morris, Professor of Statistics (Director of Graduate Studies)
Donald B. Rubin, John L. Loeb Professor of Statistics
Kenneth E. Stanley, Lecturer on Statistics (FAS) and Lecturer on Biostatistics (Public Health)

Other Faculty Offering Instruction in the Department of Statistics

Arthur P. Dempster, Research Professor of Theoretical Statistics
Daniel James Greiner, Assistant Professor of Law (Law School)
David P. Harrington, Professor of Biostatistics (Public Health) (Director of Undergraduate Studies)
Guido W. Imbens, Professor of Economics (on leave spring term)
Xiaole Shirley Liu, Associate Professor of Biostatistics (Public Health)
Bernard Rosner, Professor of Medicine (Medical School, Public Health)
Patrick J. Wolfe, Assistant Professor of Electrical Engineering on the Gordon McKay Endowment
Alan M. Zaslavsky, Professor of Health Care Policy (Medical School)

The Statistics Department offers four courses at the introductory level (below Statistics 110), Statistics 100, 101, 102 and 104. All provide an introduction to statistics for students who have minimal or no previous course work in statistics. These four courses cover common core material in statistical inference, but differ in the amount of emphasis placed on different application areas and techniques. The Department also offers an intermediate course (Statistics 105) below Statistics 110 for which any of the four introductory courses may serve as a prerequisite.

Statistics 100 provides a general introduction to statistics for students concentrating in the humanities, the social sciences or those who are undecided about their concentration. Statistics 100 emphasizes the underlying ideas of probability and statistical inference, including multiple regression, and illustrates the use of statistics across a wide range of disciplines. Statistics 101 emphasizes analysis of variance and other topics commonly used in experimental psychology.

Students planning to concentrate in Psychology are encouraged to enroll in Statistics 101. Statistics 102 introduces material typically used in biomedical research and epidemiology and is intended for students planning to attend medical school. Statistics 104 emphasizes techniques used in economics and finance, including multiple regression, and the analysis of categorical data. Statistics 104 moves at a faster pace than 100, 101 or 102, and is the preferred first course for students intending to concentrate in Economics.

Statistics 105 is a second course in statistics for students wishing to explore in more depth statistical applications to problems that arise in everyday life, such as the evaluation of new drugs or in matching algorithms used by on-line dating services. Students work in teams on a substantive problem and learn to write a scientific proposal, design an experiment, analyze and present results.

Statistics 101 and 104 will be accepted as fulfilling any requirement or prerequisite that is fulfilled by Statistics 100. Consult the Statistics Department or your tutorial office for more information about courses that satisfy your concentration requirements and for guidance on selecting a course. More detailed information can be accessed at the Statistics Department website: www.stat.harvard.edu.

Primarily for Undergraduates

*Statistics 91r. Supervised Reading and Research
Catalog Number: 6641
Joseph K. Blitzstein, David P. Harrington, and members of the Department
Half course (fall term; repeated spring term). Hours to be arranged.
Note: Normally may not be taken more than twice; may be counted for concentration in Statistics, if taken for graded credit; may be taken in either term; for further information consult Director or Assistant DUS.

*Statistics 99hf. Tutorial — Senior Year
Catalog Number: 4381
Joseph K. Blitzstein, David P. Harrington, and members of the Department
Half course (throughout the year). Hours to be arranged.
The systematic application of statistical ideas to a problem area.
Note: In exceptional circumstances, may be taken as a half course in the spring term only; for further information consult Director or Assistant DUS.

For Undergraduates and Graduates

Statistics 100. Introduction to Quantitative Methods for the Social Sciences and Humanities
Catalog Number: 3808
Mark E. Glickman (Boston University) (fall term) and David P. Harrington (Public Health) (spring term)
Half course (fall term; repeated spring term). Fall: M., W., F., at 10, and weekly sections to be arranged; Spring: M., W., F., at 11, and weekly sections to be arranged. EXAM GROUP: Fall: 3; Spring: 4
Introduction to key ideas underlying statistical and quantitative reasoning. Topics covered: methods for organizing, summarizing and displaying data; elements of sample surveys, experimental design and observational studies; methods of parameter estimation and hypothesis testing in one- and two-sample problems; regression with one or more predictors; correlation; and analysis of variance. Explores applications in a wide range of fields, including the social and political sciences, medical research, and business and economics.
Note: Only one of the following courses may be taken for credit: Statistics 100, 101, 104. When taken for a letter grade, this course meets the Core area requirement for Quantitative Reasoning.

Statistics 101. Introduction to Quantitative Methods for Psychology and the Behavioral Sciences
Catalog Number: 5128
Alan Agresti (University of Florida) (fall term) and Julia Yi-Hsin Lin (Medical School) (spring term)
Half course (fall term; repeated spring term). Fall: Tu., Th., 2:30–4; Spring: Tu., Th., 11:30–1. EXAM GROUP: Fall: 16, 17; Spring: 13, 14
Similar to Stat 100, but emphasizes concepts and practice of statistics used in psychology and other behavioral sciences. Topics covered: measures of central tendency and variability; development of scales used in behavioral sciences; probability; correlation and regression; estimation and hypothesis testing; analysis of variance; and chi-square tests for cross-classified data. Emphasis on translation of research questions into statistically testable hypotheses and interpretation of results in context of original research questions.
Note: Only one of the following courses may be taken for credit: Statistics 100, 101, 104. When taken for a letter grade, this course meets the Core area requirement for Quantitative Reasoning.

Statistics 102. Fundamentals of Biostatistics
Catalog Number: 0266
Bernard Rosner (Medical School, Public Health)
Half course (fall term). M., W., F., at 11, and a weekly section to be arranged. EXAM GROUP: 4
An introduction to statistical methods used in biological and medical research. Elementary probability theory, basic concepts of statistical inference, sampling theory, regression and correlation methods, analysis of variance, and study design. Emphasis on applications to medical problems.
Note: Primarily for undergraduates with medical or biological interests. When taken for a letter grade, this course meets the Core area requirement for Quantitative Reasoning.

Statistics 104. Introduction to Quantitative Methods for Economics
Catalog Number: 4582
Kenneth E. Stanley (FAS, Public Health)
Half course (fall term; repeated spring term). Lecture 1: M., W., F., at 11; Lecture 2: M., W., F., at 1, and weekly sections to be arranged. EXAM GROUP: 4
Similar to Stat 100, but emphasizes applications in fields including, but not limited to, economics, health sciences and policy analysis. Topics covered: descriptive and summary statistics for both measured and counted variables; elements of experimental and survey design; probability; and statistical inference including estimation and tests of hypotheses as applied to one- and two-sample problems, multiple regression, correlation, and analysis of variance. Taught at a slightly higher level than Stat 100 and 101.
Note: Only one of the following courses may be taken for credit: Statistics 100, 101, 104. When taken for a letter grade, this course meets the Core area requirement for Quantitative Reasoning.

Statistics 105. Real-Life Statistics: Your Chance for Happiness (or Misery)
Catalog Number: 8782
Xiao-Li Meng
Half course (spring term). M., W., 1–2:30. EXAM GROUP: 6, 7
Discover an appreciation of statistical principles and reasoning via "Real-Life Modules" that can make you rich or poor (financial investments), loved or lonely (on-line dating), healthy or ill (clinical trials), satisfied or frustrated (chocolate/wine tasting) and more. Designed for those for whom this could be their last statistics course or those who want to be inspired to learn more from a subject that can intimately affect their chance for happiness (or misery) in life.
Note: This course, when taken for a letter grade, meets the Core requirement for Quantitative Reasoning.
Prerequisite: Stat 100 or equivalent or another course in statistics with permission of the instructor.

Statistics 110. Introduction to Probability
Catalog Number: 0147
Joseph K. Blitzstein
Half course (fall term). M., W., F., at 12, and a weekly section to be arranged. EXAM GROUP: 5
A comprehensive introduction to probability. Basics: sample spaces and events, conditional probability, and Bayes’ Theorem. Univariate distributions: density functions, expectation and variance, Normal, t, Binomial, Negative Binomial, Poisson, Beta, and Gamma distributions. Multivariate distributions: joint and conditional distributions, independence, transformations, and Multivariate Normal. Limit laws: law of large numbers, central limit theorem. Markov chains: transition probabilities, stationary distributions, convergence.
Note: When taken for a letter grade, this course meets the Core area requirement for Quantitative Reasoning.
Prerequisite: Mathematics 19a or equivalent or above required (may be taken concurrently), Mathematics 19b or equivalent or above recommended.

Statistics 111. Introduction to Theoretical Statistics
Catalog Number: 1836
S.C. Samuel Kou
Half course (spring term). Tu., Th., 1–2:30, and a weekly section to be arranged. EXAM GROUP: 15, 16
Basic concepts of statistical inference from frequentist and Bayesian perspectives. Topics include maximum likelihood methods, confidence and Bayesian interval estimation, hypothesis testing, least squares methods and categorical data analysis.
Prerequisite: Mathematics 19a and 19b or equivalent and Statistics 110.

Statistics 115. Introduction to Computational Biology and Bioinformatics
Catalog Number: 9776
Xiaole Shirley Liu (Public Health) and Jun S. Liu
Half course (spring term). Tu., Th., 11:30–1. EXAM GROUP: 13, 14
Basic problems, algorithms and data analysis approaches in computational biology. Topics include sequence alignment, genome sequencing and gene finding, gene expression microarray analysis, transcription regulation and sequence motif finding, comparative genomics, RNA/protein structure prediction, proteomics and SNP analysis. Computational algorithms covered include hidden Markov model, Gibbs sampler, clustering and classification methods.
Prerequisite: Good quantitative skills, strong interest in biology, willingness and diligence to learn programming.

[Statistics 120. Intermediate Biostatistical Methods]
Catalog Number: 7200
Bernard Rosner (Medical School, Public Health)
Half course (spring term). M., W., F., at 11, and a weekly section to be arranged. EXAM GROUP: 4
A survey of multivariable methods used in medical and biological research. A review of univariate inference, multiple regression, analysis of variance, nonparametric methods, logistic regression, elements of study design, survival analysis, and selected special topics in biostatistics. Emphasis on application to medical problems.
Note: Expected to be given in 2009–10. Primarily for undergraduates with medical or biological interests.
Prerequisite: Either Statistics 100, 102, 104, or Statistics 110, 111.

Statistics 131. Time Series Analysis and Forecasting
Catalog Number: 8291
Tirthankar Dasgupta
Half course (fall term). Tu., Th., 11:30–1. EXAM GROUP: 13, 14
An introduction to time series models and associated methods of data analysis and inference. Auto regressive (AR), moving average (MA), ARMA, and ARIMA processes, stationary and non-stationary processes, seasonal processes, auto-correlation and partial auto-correlation functions, identification of models, estimation of parameters, diagnostic checking of fitted models, forecasting, spectral analysis, and transfer function models.
Prerequisite: Statistics 111 and 139 or equivalent.

Statistics 135. Statistical Computing Software
Catalog Number: 3451
Steven Richard Finch
Half course (fall term). M., W., F., at 10. EXAM GROUP: 3
An introduction to major statistics packages used in academics and industry (SAS and R). Will discuss data entry and manipulation, implementing standard analyses and graphics, exploratory data analysis, simulation-based methods, and new programming methods.
Prerequisite: Statistics 100 or 139 (may be taken concurrently) or with permission of instructor.

Statistics 139. Statistical Sleuthing Through Linear Models
Catalog Number: 1450
----------
Half course (fall term). Tu., Th., 10-11:30, and a weekly section to be arranged. EXAM GROUP: 12, 13
A serious introduction to statistical inference where linear models and related methods are used. Topics include the pros and cons of t-tools and their alternatives, multiple-group comparisons, linear regressions, model checking and refinement. Emphasis on statistical thinking and tools for real-life problems, application to current events whenever relevant.
Prerequisite: Statistics 100 or equivalent and Mathematics 19a and 19b or equivalent.

Statistics 140. Design of Experiments
Catalog Number: 7112
Tirthankar Dasgupta
Half course (spring term). Tu., Th., 11:30–1. EXAM GROUP: 13, 14
Statistical designs for efficient experimentation in physical, chemical, biological, social and management sciences and in engineering. A systematic approach to explore input-output relationships by deliberately manipulating input variables. Topics include analysis of variance, completely randomized and randomized block designs, Latin square designs, balanced incomplete block designs, factorial designs, confounding in blocks, fractional replications, orthogonal arrays, and response surface designs. Each topic is motivated by a real-life example.
Prerequisite: Statistics 100 or equivalent and Mathematics 19a and 19b.

Statistics 149. Statistical Sleuthing through Generalized Linear Models
Catalog Number: 6617
----------
Half course (spring term). M., W., 2:30–4. EXAM GROUP: 7, 8
A sequel to Statistics 139, emphasizing common methods for analyzing categorical data. Topics include mixed effects model, contingency tables, log-linear models, logistic, Probit and Poisson regression, model selection, and model checking. Examples will be drawn from several fields, particularly from biology and social sciences.
Prerequisite: Statistics 139 or permission of instructor.

[Statistics 155. Spatial Statistics for Social Inquiry and Health Research]
Catalog Number: 1993
Christopher J. Paciorek (Public Health) and Louise M. Ryan (Public Health)
Half course (spring term). Hours to be arranged.
Introduction to spatial statistics as applied to social science and public health. Emphasizes analysis and visualization methods for areal data, geostatistical data, and point processes. Practical focus on case studies, guest lectures, and student projects.
Note: Expected to be given in 2009–10. Basic GIS skills will be covered in a short module. Offered jointly with the School of Public Health as BIO 283. May not be taken for credit if Biostatistics 283 has already been taken. May not be taken concurrently with Biostatistics 283.
Prerequisite: Coursework or equivalent experience in regression at the level of Statistics 139 or 149, Economics 1123, Psychology 1951, Biostatistics 210, 211, or 213, and coursework or equivalent experience in statistical programming such as Statistics 135 or Biostatistics 503 or permission of instructors. Prerequisites are guidelines and students are encouraged to consult instructors.

[Statistics 160. Design and Analysis of Sample Surveys]
Catalog Number: 2993
Alan M. Zaslavsky (Medical School)
Half course (fall term). Hours to be arranged.
Methods for design and analysis of sample surveys. The toolkit of sample design features and their use in optimal design strategies. Sampling weights and variance estimation methods, including resampling methods. Brief overview of nonstatistical aspects of survey methodology such as survey administration and questionnaire design and validation (quantitative and qualitative). Additional topics: calibration estimators, variance estimation for complex surveys and estimators, nonresponse, missing data, hierarchical models, and small-area estimation.
Note: Expected to be given in 2009–10.
Prerequisite: Statistics 111 or 139 or permission of instructor.

Statistics 170. Introduction to Quantitative Methods in Finance
Catalog Number: 1202
Yoonjung Lee
Half course (spring term). Tu., Th., 10–11:30, and a weekly section to be arranged. EXAM GROUP: 12, 13
Introduces stochastic analysis tools to be used as a basis for developing continuous-time asset pricing theory. Various quantitative methods widely used in the financial industry for valuing derivative products will be presented: binomial-tree valuation methods, extensions of the Black-Scholes option pricing formula, numerical techniques for solving partial differential equations, and Monte Carlo simulations.
Prerequisite: Statistics 110 and 111 or equivalent.

Statistics 171. Introduction to Stochastic Processes
Catalog Number: 4180
Jun S. Liu
Half course (spring term). Tu., Th., 2:30–4, and a weekly section to be arranged. EXAM GROUP: 16, 17
An introductory course in stochastic processes. Topics include Markov chains, branching processes, Poisson processes, birth and death processes, Brownian motion, martingales, introduction to stochastic integrals, and their applications.
Prerequisite: Statistics 110 or equivalent.

Primarily for Graduates

Statistics 210. Probability Theory
Catalog Number: 2487
Carl N. Morris and Joseph K. Blitzstein
Half course (fall term). Tu., Th., 1–2:30. EXAM GROUP: 15, 16
Random variables, measure, representations. Families of distributions: Multivariate Normal, conjugate, marginals, mixtures. Conditional distributions and expectation. Convergence, laws of large numbers, and central limit theorems. Markov chains and martingales.
Prerequisite: Statistics 110 or equivalent required; Statistics 111 or equivalent recommended.

Statistics 211. Statistical Inference
Catalog Number: 1946
Carl N. Morris
Half course (spring term). Tu., Th., 1–2:30. EXAM GROUP: 15, 16
Inference: frequency, Bayes, decision analysis, foundations. Likelihood, sufficiency, and information measures. Models: Normal, exponential families, multilevel, and non-parametric. Point, interval and set estimation; hypothesis tests. Computational strategies, large and moderate sample approximations.
Prerequisite: Statistics 111 and 210 or equivalent.

[Statistics 212. Probability and Mathematical Statistics III: Special Topics]
Catalog Number: 7864
----------
Half course (fall term). F., 2–5. EXAM GROUP: 7, 8, 9
Contemporary probabilistic techniques for analysis of stochastic processes commonly used in applied probability. Studies functional weak convergence analysis and large deviations results (both for light and heavy-tailed systems). Applications: Queueing, Risk Theory, Finance, and Biology.
Note: Expected to be given in 2009–10.

Statistics 220. Bayesian Data Analysis
Catalog Number: 6270
Donald B. Rubin and S.C. Samuel Kou
Half course (fall term). Tu., Th., 2:30–4. EXAM GROUP: 16, 17
Basic Bayesian models, followed by more complicated hierarchical and mixture models with nonstandard solutions. Includes methods for monitoring adequacy of models and examining sensitivity of models.
Note: Emphasis throughout term on drawing inferences via computer simulation rather than mathematical analysis.
Prerequisite: Statistics 110 and 111.

Statistics 221. Applied Bayesian Statistical Computing
Catalog Number: 5959
Jun S. Liu
Half course (spring term). Tu., Th., 10–11:30. EXAM GROUP: 12, 13
Computing methods commonly used in statistics: Generation of random numbers, Monte Carlo methods, optimization methods, numerical integration and advanced Bayesian computational tools such as the Gibbs sampler, Metropolis Hastings, method of auxiliary variables, marginal and conditional data augmentation, slice sampling, exact sampling and reversible jump MCMC.
Note: Computer programming exercises will apply the methods discussed in class.
Prerequisite: Linear algebra, Statistics 111, and knowledge of a computer programming language required; Statistics 220 recommended.

[Statistics 225. Spatial Statistics]
Catalog Number: 6499
----------
Half course (fall term). M., W., 1–2:30. EXAM GROUP: 6, 7
Introduction to three types of spatial data: point pattern, geospatial, and lattice. For each type of data, presentation and application of statistical and computational methods for description, modeling, and analysis.
Note: Expected to be given in 2009–10.

Statistics 230. Multivariate Statistical Analysis
Catalog Number: 5206
Edoardo Maria Airoldi
Half course (spring term). M., W., 1–2:30. EXAM GROUP: 6, 7
Multivariate inference and data analysis. Advanced matrix theory and distributions, including Multivariate Normal, Wishart, and multilevel models. Supervised learning: multivariate regression, classification, and discriminant analysis. Unsupervised learning: dimension reduction, principal components, clustering, and factor analysis.
Prerequisite: Statistics 211 or equivalent.

Statistics 231. Time Series Analysis and Forecasting
Catalog Number: 7537
Tirthankar Dasgupta
Half course (fall term). Tu., Th., 11:30–1. EXAM GROUP: 13, 14
Meets with Statistics 131, but graduate students will be exposed to a more rigorous treatment of time series analysis.
Prerequisite: Statistics 111 and 139 or equivalent.

[Statistics 233. Matched Sampling]
Catalog Number: 4036
Donald B. Rubin
Half course (fall term). Tu., Th., 11:30–1. EXAM GROUP: 13, 14
This course provides an accessible introduction to the study of matched sampling in economics, education, epidemiology, medicine, political science, psychology, sociology, statistics, or any field conducting empirical research to evaluate the causal effects of interventions.
Note: Expected to be given in 2009–10.

Statistics 239. Statistical Sleuthing Through Linear Models
Catalog Number: 8433
----------
Half course (fall term). Tu., Th., 10–11:30 and a weekly section to be arranged. EXAM GROUP: 12, 13
Meets with Statistics 139, but graduate students will be required to complete additional assignments designed to cover theoretical aspects of regression analysis.

[Statistics 245. Statistics and Litigation]
Catalog Number: 3488
Daniel James Greiner (Law School)
Half course (spring term). Tu., Th., 5–6:30. EXAM GROUP: 18
Students work in teams with law students to analyze data, prepare expert reports, and give testimony. Course teaches how to analyze data, present results to untrained but intelligent users, and defend conclusions.
Note: Expected to be given in 2009–10.
Prerequisite: A graduate course in data analysis, such as Statistics 220, Government 2001, or Economics 2120

Statistics 249. Statistical Sleuthing Through Generalized Linear Models
Catalog Number: 3987
----------
Half course (spring term). M., W., 2:30–4. EXAM GROUP: 7, 8
Meets with Statistics 149, but graduate-level covers supplementary topics such as Bayesian analysis for generalized linear models and generalized mixed effect models. Requires extra homework and examination problems in addition to those for Statistics 149.
Prerequisite: Statistics 139, Statistics 220 or Statistics 221, or by permission of instructor.

Statistics 270. Introduction to Quantitative Methods in Finance
Catalog Number: 3518
Yoonjung Lee
Half course (spring term). Tu., Th., 10–11:30, and a weekly section to be arranged. EXAM GROUP: 12, 13
Meets with Statistics 170, but graduate students will be exposed to a more rigorous treatment of stochastic calculus.
Prerequisite: Statistics 110 and 171 or equivalent.

Cross-listed Courses

Biostatistics 244. Analysis of Failure Time Data
[*Biostatistics 250. Probability Theory and Applications II]
Economics 1127. Statistical Methods for Evaluating Causal Effects
*Government 3009. Research Workshop in Applied Statistics

Graduate Courses of Reading and Research

*Statistics 301. Special Reading and Research
Catalog Number: 4474
Joseph K. Blitzstein 5588, Tirthankar Dasgupta 5765, Arthur P. Dempster 2345, S.C. Samuel Kou 4054, Yoonjung Lee 5300, Jun S. Liu 3760, Xiao-Li Meng 4023, Carl N. Morris 2178, Bernard Rosner (Medical School, Public Health) 4018, Donald B. Rubin 7966, Patrick J. Wolfe 5144, and Alan M. Zaslavsky (Medical School) 1927
Half course (fall term; repeated spring term). Hours to be arranged.

*Statistics 302. Direction of Doctoral Dissertations
Catalog Number: 3382
Joseph K. Blitzstein 5588, Tirthankar Dasgupta 5765, Arthur P. Dempster 2345, S.C. Samuel Kou 4054, Yoonjung Lee 5300, Jun S. Liu 3760, Xiao-Li Meng 4023, Carl N. Morris 2178, Bernard Rosner (Medical School, Public Health) 4018, Donald B. Rubin 7966, Patrick J. Wolfe 5144, and Alan M. Zaslavsky (Medical School) 1927
Half course (fall term; repeated spring term). Hours to be arranged.

*Statistics 303hf. The Art and Practice of Teaching Statistics
Catalog Number: 3545
Xiao-Li Meng 4023 and Joseph K. Blitzstein 5588
Half course (throughout the year). M., 10–12.
Required of all first-year doctoral students in Statistics.

*Statistics 310hfr. Topics in Astrostatistics
Catalog Number: 2105
Xiao-Li Meng 4023
Half course (throughout the year). Tu., 11:30–1.

[*Statistics 311. Monte Carlo Methods in Scientific Computing]
Catalog Number: 0826
Jun S. Liu 3760
Half course (fall term). Hours to be arranged.
Note: Expected to be given in 2009–10.
Prerequisite: Statistics 220 or equivalent.

[*Statistics 315. Statistical Computing and Computational Biology]
Catalog Number: 0553
Jun S. Liu 3760
Half course (spring term). .
Note: Expected to be given in 2009–10.

*Statistics 321. Stochastic Modeling and Bayesian Inference
Catalog Number: 4060
S.C. Samuel Kou 4054
Half course (spring term). Hours to be arranged.
Stochastic processes and their applications in biological, chemical and financial modeling. Bayesian inference about stochastic models based on the Monte Carlo sampling approach.

*Statistics 324r. Parametric Statistical Inference and Modeling
Catalog Number: 3366
Carl N. Morris 2178
Half course (spring term). Th., 3–5.
Theory of multi-level parametric models, including hidden Markov models, and applications likely to include biostatistics, health services, education, and sports.

*Statistics 332. Topics in Missing Data
Catalog Number: 9483
Donald B. Rubin 7966
Half course (fall term). Hours to be arranged.

*Statistics 340. Random Network Models
Catalog Number: 1650
Joseph K. Blitzstein 5588
Half course (spring term). Hours to be arranged.
Random graph models for biological, social, and information networks, including fixed degree, exponential, power law, small world, and geometric random graphs. Estimation and sampling methods for network data.

*Statistics 341. Advanced Topics in Experimental Design - (New Course)
Catalog Number: 9827
Tirthankar Dasgupta 5765
Half course (fall term). W., 2–4.

*Statistics 370. Topics in Empirical Finance
Catalog Number: 3593
Yoonjung Lee 5300
Half course (spring term). M., 2:30–4.
Exposes students to a variety of topics in Empirical Finance, including high frequency data analysis, high-dimensional volatility estimation, continuous-time stochastic modeling, and non-linear filtering.

*Statistics 385. Topics in Statistical Machine Learning - (New Course)
Catalog Number: 0512
Edoardo Maria Airoldi
Half course (spring term). W., 2:30–5:30.
Hands-on introduction to concepts and computations central to cutting-edge research, including sparse learning in high dimensions, semi-supervised learning strategies, structured predictions, approximate inference strategies in probabilistic graphical models, and statistical elements of graph data analysis.

*Statistics 399hf. Problem Solving in Statistics
Catalog Number: 1035
Carl N. Morris 2178
Half course (throughout the year). W., 4:30–6.
Aimed principally at helping Statistics PhD students beyond their first year transition through the qualifying exams into research.