course | number | instructor | level | time |
---|---|---|---|---|
Introduction to Statistics | 101-106a | Chang et al | intro, no prereqs | T,Th 1:00-2:15 |
Probability Theory | 241/541a | Wegkamp | intro, with calculus | M,W,F 9:30-10:20 |
Linear Models | 312/612a | Radulovic | intermediate | T,Th 9:00-10:15 |
Stochastic Calculus | 603a | Pollard | adv. grad | T, Th 10:30-11:50 |
Statistical Inference | 610a | Pollard | intro grad | M, W 1:00-2:20 |
Statistical Case Studies | 625a | Hartigan | intermediate grad | M 1:00 - 3:30 |
Topics in the Statistical Analysis of Genomic Data | 646a | Chang | M, W 11:00-12:15 | |
Analysis of Spatial & Time Series Data | 374/664a | Hartigan | T, Th 1:00-2:15 | |
Introductory Data Analysis | 230/530b | Hartigan | intro | M,W 1:00 - 2:15, 2:30-3:45 |
Theory of Statistics | 242/542b | Barron | intro, with calculus | M,W,F 9:30-10:20 |
Stochastic Processes | 251/551b | Radulovic | intermediate | M,W 1:00-2:15 |
Advanced Probability | 330/600b | Wegkamp | adv. undergrad/
intermediate grad |
T,Th 2:30-3:45 |
Data Analysis | 361/661b | Hengartner | intermediate | M,W 2:30-3:45 |
Information Theory | 364/664b | Barron | intermediate | T,Th 9:00-10:15 |
Intro. to Function Estimation | 365/665b | Hengartner | M,W 11:30-12:45 | |
Monte Carlo Methods | 368/668b | Radulovic | M,W 9:00 - 10:15 | |
Applied Math Senior Seminar | AM490b | Chang | W 3:30 - 5:20 | |
Practical Work | 626b | Pollard | adv. grad | TBA |
Nonparametric Statistics | 680b | Wegkamp | W 10:30-11:30, F 10:30 - 12:30 | |
Research Seminar in Statistics | 699b | Pollard | Th 10:30 - 12:30,
M 1:00 - 2:00 as scheduled (see below) |
STAT 101a-106a,
Introduction to Statistics (FALL)
Cross-listing: Statistics 501a-506a
Instructor: Mr. Joseph Chang and faculty from
other departments.
Time: Tues, Thurs 1:00 pm - 2:15 pm
Place: OML 202
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 Tuesday lecture, which introduces general concepts and methods of statistics,
is attended by all students in Statistics 101-106 together. The course
separates for Thursday lectures (sections), which 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. They do not count toward the natural sciences requirement.
No prerequisites beyond high school algebra.
STAT 101a / E&EB 210a / MCDB 215a, Introduction
to Statistics: Life Sciences.
Instructor: Mr. Joseph Chang/ Mr. Junhyong
Kim.
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. Joseph Chang/Mr. John Lapinski.
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 104a / PSYC 201a, Introduction to Statistics:
Psychology.
Instructor: Mr. Joseph Chang/Mr. Thomas Brown.
Statistical and probabilistic analysis of psychological
problems presented with a unified foundation in basic statistical theory.
The problems are drawn from studies of sensory processing and perception,
development, learning, and psychopathology.
STAT 105a / F&ES 205a, Introduction to
Statistics: Environmental Sciences.
Instructor: Mr. Joseph Chang/Mr. Jonathan
Reuning-Scherer.
An introduction to probability and statistics
with emphasis on applications to forestry and environmental sciences.
STAT 106a, Introduction to Statistics: Data
Analysis.
Instructor: Mr. Joseph Chang/Mr. Nicolas
Hengartner.
An introduction to probability and statistics
with emphasis on data analysis.
[MORE COURSE INFORMATION]
STAT 241a, Probability
Theory (FALL)
Cross-listing: Statistics/Mathematics 541a
Instructor: Mr. Marten Wegkamp
Time: Mon, Wed, Fri 9:30 - 10:20
Place: WLH 116
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.
[MORE COURSE INFORMATION]
STAT 242b,
Theory of Statistics (SPRING)
Cross-listing: Statistics 542b, Mathematics
242b
Instructor: Mr. Andrew Barron
Time: Mon, Wed, Fri 9:30 - 10:20
Place: BCT 102/15 Prospect
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. Dragan Radulovic
Time: Mon, Wed 1 - 2:15
Place: BCT C031/15 Prospect
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. After Statistics 241a
or equivalent.
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COURSE INFORMATION]
STAT 312a, Linear
Models (FALL)
Cross-listing: Statistics 612a
Instructor: Mr. Dragan Radulovic
Time: Tues, Thurs 9:00-10:15
Place: 24 Hillhouse Avenue, 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. After Statistics
242b and Mathematics 222 or equivalents.
[MORE
COURSE INFORMATION]
STAT 361b,
Data Analysis (SPRING)
Cross-listing: Statistics 661b
Instructor: Mr. Nicolas Hengartner
Time: Mon, Wed 2:30 - 3:45
Place: AKW 400/51 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. Weekly
sessions will be held in the Social
Sciences Statistical Laboratory. After Statistics 242
and Mathematics 222b or 225a or b, or equivalents.
[MORE
COURSE INFORMATION]
STAT 364b,
Information Theory (SPRING)
Cross-listing: Statistics 664b
Instructor: Mr. Andrew Barron
Time: Tue, Thu 9:00 - 10:15
Place: **Location Change** 24
Hillhouse, 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.
[MORE
COURSE INFORMATION]
STAT 365b,
Introduction to Function Estimation (SPRING)
Cross-listing: Statistics 665b
Instructor: Mr. Nicolas Hengartner
Time: Mon, Wed 11:30 - 12:45
Place: 24 Hillhouse, Room 107
A practical introduction to curve estimation
techniques, such as non-linear regression, and non-parametric regression.
Splines, local smoothers and neural networks will be discussed and applied
to data. Further topics include model selection, pattern recognition, inverse
problems and density estimation. SPLUS is used.
[MORE
COURSE INFORMATION]
STAT 368b,
Monte Carlo Methods (SPRING)
Cross-listing: Statistics 668b
Instructor: Mr. Dragan Radulovic
Time: Mon, Wed 9:00 - 10:15
Place: 24 Hillhouse, Room 107
Monte Carlo methods provide approximate solution
to a variety of mathematical problems by performing random sampling experiments
on a computer. This course will cover classical applications like integration,
maximization, root finding as well as some modern developments related
to statistics, including bootstrapping. The course will address both theory
and application. Knowledge of at least one computer programming language
will be required (CPSC 112 or equivalent). Additional prerequisites are
MATH 120 and STAT 241 or equivalent.
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COURSE INFORMATION]
STAT 374a, Analysis
of Spatial and Time Series Data (FALL)
Cross-listing: Statistics 674a
Instructor: Mr. John Hartigan
Time: Tue, Thu 1:00 - 2:15
Place: 24 Hillhouse Avenue, Room 107
Study of statistical models that are useful for
describing data collected over space or time. Models include frequency
domain and time domain analysis of time series; state space models and
Kalman filters; point processes; Gibbs processes and random fields. After
Statistics 241a, 242b or permission of instructor.
[MORE
COURSE INFORMATION]
AM490b,
Applied Math Senior Seminar and Project (SPRING)
Cross-listing:
Instructor: Mr. Joseph Chang
Time: Wed 3:30 - 5:20
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.
[MORE
COURSE INFORMATION]
STAT 600b,
Advanced Probability (SPRING)
Cross-listing: Statistics 330b
Instructor: Mr. Marten Wegkamp
Time: Tues, Thurs 2:30 - 3:45
Place: 24 Hillhouse Avenue, Room 107
Measure theoretic probability, conditioning,
laws of large numbers, convergence in distribution, characteristic functions,
central limit theorems, martingales. Some knowledge of real analysis is
assumed.
[MORE
COURSE INFORMATION]
STAT 603a, Stochastic
Calculus (FALL)
Instructor: Mr. David Pollard
Time: Tues, Thurs 10:30 am - 11:50 am
Place: 24 Hillhouse Avenue, 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.
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COURSE INFORMATION]
STAT 610a, Statistical
Inference (FALL)
Instructor: Mr. David Pollard
Time: Mon, Wed 1:00 pm - 2:20 pm
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.
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COURSE INFORMATION]
STAT 625a, Statistical
Case Studies (FALL)
Instructor: Mr. John Hartigan
Time: Monday 1:00 pm - 3:30 pm
Place: 24 Hillhouse Avenue, Room 211
Thorough study of some large data sets on such
topics as second-hand smoking, crashes in small cars, reticulate evolution,
bloc voting, and Connecticut educational standards.
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COURSE INFORMATION]
STAT 626b,
Practical Work (SPRING)
Instructor: Mr. David Pollard
Individual one-semester projects, with students
working on studies outside the Department, under the guidance of a statistician.
[MORE
COURSE INFORMATION]
STAT 646a, Topics
in the Statistical Analysis of Genomic Data (FALL)
Instructors: Mr. Joseph Chang and Mr. Junhyong
Kim
Time: Mon, Wed 11:00 am - 12:15 pm
Place: 24 Hillhouse Avenue, Room 107
Several recently developed statistical methods
have either already played an important role in the analysis of genomic
and post-genomic data, or appear to be promising candidates to do so. We
will study hidden Markov models, Bayesian networks, support vector machines
and kernel methods, and perhaps other topics to be determined. For each
topic, instructors will present introductory lectures on the statistical
theory, models, and methods of analysis. Students will work on projects
and present results, which may include computer implementations of the
statistical techniques, analyses of biological sequence and gene expression
data using available programs, and reports on research papers. Although
no specific prerequisite courses are required, the course will make a substantial
use of probability theory, statistics, introductory biology, and computation.
Students without background in some of these areas may need to do additional
work and should consult the instructors before enrolling.
[MORE
COURSE INFORMATION]
STAT 680b, Nonparametric
Statistics (SPRING)
Instructor: Mr. Marten Wegkamp
Time: Wed 10:30 - 11:30, Fri 10:30 -
12:30
Place: 24 Hillhouse, Room 107
We discuss recent theoretical developments in
nonparametric regression, density estimation and classification. We introduce
some basic empirical process theory and related tricks. Emphasis will be
put on universal consistency and model selection. There is no required
textbook, although I will use various sources: monographs by Devroye and
Lugosi (2001), Van de Geer (2000), combined with articles, and ongoing
research. Prerequisite: STAT 330b/600b.
[MORE
COURSE INFORMATION]
STAT 699b, Research Seminar
in Statistics (SPRING)
Instructor: Mr. David Pollard
Time: Thurs 10:30 - 12:30, Occasionally
Mon 1:00 - 2:00 as scheduled (see more info. below)
Place: 24 Hillhouse, Room 107
An introduction to some current research topics,
built around the weekly Departmental seminar.
[MORE
COURSE INFORMATION]
STAT 700, Departmental Seminar
Time: Monday 4:15 pm - 5:30 pm
Important activity for all members of the department. 24 Hillhouse
Avenue. See weekly seminar announcements.