 
Course List for 20042005

course  number  instructor  level  time 

Introduction to Statistics  101106a  ReuningScherer & Staff  intro, no prereqs  T,Th 1:002:15 
Probability and Statistics for Scientists  238a  Chang  intro  M,W,F 2:303:20 
Probability Theory with Applications  241/541a  Leeb  intro, with calculus  M,W,F 9:3010:20 
Linear Models  312/612a  Leeb 
intermediate  T,Th 9:0010:15 
Data Analysis  361/661a  Emerson  intermediate  M, W 2:303:45 
Stochastic Calculus  603a  Pollard  please see course listing below 

Foundations of
Statistics 
605a  Hartigan  M,W 1:002:15 

Statistical Inference  610a  Zhou  intro grad  T,Th 10:3011:45 
Statistical Case Studies  625a  Emerson/Hartigan  intermediate grad  T,Th 2:303:45 
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 2:303:45 
Theory of Statistics  242/542b  Zhou/Barron  intro, with calculus  M,W,F 9:3010:20 
Stochastic Processes  251/551b  Chang  intermediate  M,W 1:002:15 
Advanced Probability  330/600b  Pollard  adv. undergrad/ intermediate grad 
T,Th 2:303:45 
Information Theory  364/664b  Yeh 
intermediate  T,Th 9:0010:15 
Data Mining and Machine Learning  365/665b  Leeb  M,W 11:3012:45  
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  ReuningScherer  T, TH 1:00  2:15  
Probabilistic Networks, Algorithms, and
Applications 
667b 
Tatikonda 
T, TH 11:30  12:45 
STAT 101a / E&EB 210aG / MCDB
215a,
Introduction to Statistics: Life Sciences.
Instructor: Mr. Jonathan ReuningScherer
/ 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 ReuningScherer
/ 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 104a / PSYC 201a,
Introduction to
Statistics: Psychology. Not
offered this semester.]
STAT 105a, Introduction to
Statistics: Medicine.
Instructor: Mr. Jonathan
ReuningScherer / 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
238a,
Probability and Statistics for Scientists (FALL)
Crosslisting: Statistics 538a
Instructor: Mr. Joseph Chang
Time: Mon, Wed, Fri 2:303:20
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)
Crosslisting: 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)
Crosslisting: 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)
Crosslisting: 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)
Crosslisting: Statistics 612a
Instructor: Mr. Hannes Leeb
Time: Tues, Thurs 9:0010:15
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)
Crosslisting: 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)
Crosslisting: 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)
Crosslisting: 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)
Crosslisting:
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.
STAT
600b, Advanced Probability (SPRING)
Crosslisting: 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
605a,
Foundations of Statistics (FALL)
Instructor: Mr. John Hartigan
Time: Mon, Wed 1:002:15 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 Bayesianfrequentist
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 10:3011:45 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 2:303:45 pm
Place: 124 Prospect St,
Room B13, 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 onesemester 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 B13, 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 ReuningScherer
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, kmeans), 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)
Crosslisting:
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,
beliefpropagation, sumproduct,
and junction tree. Variational techniques: meanfield and convex
relaxations. Markov processes on graphs: MCMC, factored HMMs, and Glauber
dynamics. Some statistical physics techniques: cavity and replica methods.
Applications to errorcorrecting codes, computer vision, bioinformatics, 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 onepage 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
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.