If you are planning to enrol in a graduate course at the Department of Statistical Sciences, we recommend you read through this page carefully. Please also note, that this course schedule is subject to changes. We will post changes and updates here, so check back frequently.
Enrolment Dates

fill out the add/drop courses form

have your home department sign off on the form

email the form to our graduate student administrator
Start and End Dates of Classes & Final Examination Period
Term 
Classes Start 
Classes End 
Final Examination 

Fall Session 2019 (crosslisted courses) 
Thursday, September 5, 2019  TBD  TBD 
Fall Session 2019 (graduate courses, not crosslisted) 
Monday, September 9, 2019  TBD  TBD 
Winter Session 2020 (all courses) 
Monday, January 6, 2020  TBD  TBD 
For dates regarding university closures, course drop and registration deadlines, and tuition payment deadlines, please have a look at the School of Graduate Studies sessional dates calendar.
Course List Legend
 F = a halfyear course in the first term (September – December)
 S = a halfyear course in the second term (January– April)
 Y = a fullyear course (September – April)
 (J) indicates a a crosslisted course (a joint graduate/undergraduate course)
 M = Monday
 T = Tuesday
 W = Wednesday
 R = Thursday
 F = Friday
 See Building Codes and the Campus Map.
 L0101 or L0201 = 9:00 am to 5:00 pm
 L5101 = 5:00 pm onwards
201920 Winter Term Course Listings
The 201920 Winter Term consists of two sessions: the Fall Session 2019 and the Winter Session 2020. Please find course listings for both sessions below. You can also find a list of our graduate courses at the School of Graduate Studies page for our department.
Fall Session 2019: Course Listings
Course 
Title (Click for description) 
Session 
Section/Time 
Location 
Instructor 

(STA302H1)

Introduction to data analysis with a focus on regression. Initial Examination of data. Correlation. Simple and multiple regression models using least squares. Inference for regression parameters, confidence and prediction intervals. Diagnostics and remedial measures. Interactions and dummy variables. Variable selection. Least squares estimation and inference for nonlinear regression. Prerequisite:

F  L0101: T1012, R10; L0201: T35, R4; L0301: W25 
(L0101); BT 101 (L0201);
BR 200 (L0301)

SueChee, Shivon 
(STA304H1)

Design of surveys, sources of bias, randomized response surveys. Techniques of sampling; stratification, clustering, unequal probability selection. Sampling inference, estimates of population mean and variances, ratio estimation., observational data; correlation vs. causation, missing data, sources of bias. Exclusion: STA322H1 Prerequisite: ECO220Y1/ECO227Y1/GGR270Y1 / PSY202H1/SOC300Y1/STA221H1/STA255H1/261H1/248H1 
F  L0101: W1, F13; L0201: T122, R11 
ES 1050 (L0101);
TBA, PB B150(L0201)

Tounkara, Fode (L0101); Banjevic, Dragan (L0201) 
(STA305H1)

This crosslisted course covers a number of topics used in the design and analysis of experiments. The course is intended for students of statistics as well as students of other disciplines (eg. engineering, experimental science, etc.) who will use experimental design and analysis in their work. The course will cover the following topics: randomization, blocking Latin squares, balanced incomplete block designs, factorial experiments, confounding and fractional replication, components of variance, orthogonal polynomials, response surface methods. Additional topics will be covered based on students’ interest as time permits. Prerequisite: STA302H/352Y/ECO327Y/ECO357Y or permission of instructor. 
F  L0101: T24, R10  MS 3154  Taback, Nathan 
(STA437H1)

Practical techniques for the analysis of multivariate data; fundamental methods of data reduction with an introduction to underlying distribution theory; basic estimation and hypothesis testing for multivariate means and variances; regression coefficients; principal components and the partial multiple and canonical cor relations; multivariate analysis of variance; classification and the linear discriminant function. The use of R software should be expected. Prerequisite: STA302H/352Y Recommended Preparation: MAT223H/240H 
F  L0101: W25; L0201: T25 
MS 3153(L0101);
MS 3153 (L0201)

Molkaraie, Mehdi 
(STA480)

Statistical genetics is an important data science research area with direct impact on population health, and this course provides an introduction to its concepts and fundamentals. We start with an overview of genetic studies to have a general understanding of its goal and study design. We then introduce the basic genetic terminologies necessary for the ensuing discussion of the various statistical methods used for analyzing genetic data. The specific topics include population genetics, principles of inheritance, likelihood for pedigree data, aggregation, heritability and segregation analyses, map and linkage analysis, populationbased and familybased association studies and genomewide association studies. The flow of the content generally follows that of the The Fundamentals of Modern Statistical Genetics by Laird and Lange, and additional materials will be provided. Participating students do not need formal training in genetics, but they are expected to have statistical knowledge at the level of STA303 – Methods of Data Analysis or equivalent. 
F  L0101: M101  SS 1084  Lei, Sun 
This course will consider topics in statistics that have played a role in the development of techniques for data mining and machine learning. We will cover linear methods for regression and classification, nonparametric regression and classification methods, generalized additive models, aspects of model inference and model selection, model averaging and tree bassed methods. Prerequisite: Either STA302H or CSC411H 
F  L0101: F25  SS 1083  Brunner, Jerry  
(STA410H)

The goal of this course is to give an overview of some of the computational methods that are useful in statistics. The rst part of the course will focus on basic algorithms, such as the Fast Fourier Transform (and related methods) and methods for generating random variables. The second part of the course will focus on numerical methods for linear algebra and optimization (for example, computing least squares estimates and maximum likelihood estimates). Along the way, you will learn some basic theory of numerical analysis (computational complexity, convergence rates of algorithms) and you will encounter some statistical methodology that you may not have seen in other courses. Prerequisites: The nominal prerequisites for this course are MAT223H/240H, STA302H and CSC108H/120H/121H/148H these should give you the sucient background in both statistics and computer programming to handle the course material. A solid foundation in linear algebra is very useful for this course. 
F  L0101: M2, W13  AH 100  Knight, Keith 
STA 2111H is a course designed for Master’s and Ph.D. level students in statistics, mathematics, and other departments, who are interested in a rigorous, mathematical treatment of probability theory using measure theory. Specific topics to be covered include: probability measures, the extension theorem, random variables, distributions, expectations, laws of large numbers, Markov chains. Students should have a strong undergraduate background in Real Analysis, including calculus, sequences and series, elementary set theory, and epsilondelta proofs. Some previous exposure to undergraduatelevel probability theory is also recommended. 
F  L0101: M13, W12  Zhou, Zhou  
(STA452H)

This course is designed for graduate students in Statistics and Biostatistics. Review of probability theory, distribution theory for normal samples, convergence of random variables, statistical models, sufficiency and ancillarity, statistical functionals,influence curves, maximum likelihood estimation, computational methods. Prerequisite:

F  L0101: W1012, F10  RW 117  Brenner, David 
(STA457H1)

An overview of methods and problems in the analysis of time series data. Topics include: descriptive methods, filtering and adjustment, spectral estimation, bivariate time series models. The course will cover the following topics:
Further topics, time permitting: multivariate models; GARCH models; statespace models. 
F  L5101: T69  OI G162  Shipilov, Alex 
Overview This course is designed to provide graduate students with experience in statistical consulting. Students are active participants in research projects brought to the Statistical Consulting Service (SCS) of the Department of Statistics. The course is offered over the two sessions, fall (SeptemberDecember) and winter (JanuaryApril). The overall workload is approximately equivalent to a half graduate course and students receive a half credit. Students are not expected to have had any experience as consultants. The purpose of the course is to provide this experience so that graduates will be better able to function in such an environment when they have completed the course. The course also provides students with the opportunity to become familiar with statistical software packages such as The SAS System. There is supervision and assistance to novice consultants. Content There is some classroom instruction at the start of the term, and meetings occasionally are called to discuss special topics and for students to compare experiences. Students serve as apprentice statisticians and work under the guidance of the instructor and the SCS Coordinator on individual projects. Projects are assigned to students as they come in to the SCS. There are periods of inactivity when there are no projects and other times are very busy. The pattern of work is more like that associated with a business or working environment than a traditional course. While some consideration is taken of other academic demands on students, those enrolling must be aware that work on projects may require precedence at times. Evaluation Students will be graded on the quality of their work as stati stical consultants. This involves the ability to do work in a timely fashion, the quality of advice provided and the quality of the presentation of advice and written work to clients. Prerequisite: Students should have taken some applied statistics courses such as an undergraduate regression course. Also undergraduate courses in applied statistics, sample survey, design of experiments and time series analysis are recommended but these are not required. Also taking some of the other 2000 level applied statistics courses is recommended as this course will serve as an excellent opportunity to put the content of these courses to work. 
Y  L0101: T1012  IN 209  Taback, Nathan  
(ACT451H1)

Parametric distributions and transformations, insurance coverage modifications, limits and deductibles, models for claim frequency and severity, models for aggregate claims,stoploss insurance, risk measures. Prerequisite: Consult the instructor concerning necessary background for this course 
F  L0101: T11, R1012  UC 244  Lin, Sheldon 
Consult the instructor for further details. Prerequisite: Consult the instructor concerning necessary background for this course 
F  L0101: M912  TBA  Lin, Sheldon  
(ACT460H1)

This course is an introduction to the stochastic models used in Finance and Actuarial Science. Students will be exposed to the basics of stochastic calculus, particularly focusing on Brownian motions and simple stochastic differential equations. The role that martingales play in the pricing of derivative instruments will be investigated. Some exotic equity derivative products will be explored together with stochastic models for interest rates.
Prerequisite:

F  L0101: T25  GB 248  Lin, Sheldon 
(MMF1928H)

This course features studies in derivative pricing theory and focuses on financial mathematics and its applications to various derivative products. A working knowledge of probability theory, stochastic calculus (see e.g., STA 2502), knowledge of ordinary and partial differential equations and familiarity with the basic financial instruments is assumed. The tentative topics covered in this course include, but is not limited to:
More information: Course Website STA 2503 Prerequisite:

F  L0101: W25, M46 (p) 
SS 1085  
(CSC2537)

In this course we will study techniques and algorithms for creating effective data visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science. This course is targeted both towards students interested in using visualization in their own work, as well as students interested in building better visualization tools and systems.

F  L0101: M1012  KP 113  Chevalier, Fanny 
Please note that this course can only be taken by PhD students in the Department of Statistical Sciences. This is the Department’s core graduate course in statistical theory. It covers the basic principles of statistical inference, their application to a variety of statistical models, and some generalizations to more complex settings. Prerequisite: STA2112H and STA2212H or equivalent. (STA2111H and STA2211H may be corequisites). Some familiarity with measure theory is very useful. The text includes some supplementary material on this. 
F  L0101: T25  HS 618  Kong, Dehan / Wang, Linbo  
This course will explore Monte Carlo computer algorithms, which use randomness to perform difficult highdimensional computations. Different types of algorithms, theoretical issues, and practical applications will all be considered. Particular emphasis will be placed on Markov chain Monte Carlo (MCMC) methods. The course will involve a combination of methodological investigations, mathematical analysis, and computer programming. Prerequisite: Knowledge of statistical inference and probability theory at the advanced undergraduate level, and familiarity with basic computer programming techniques. 
F  L0101: M1012  OI 5150  Rosenthal, Jeffrey  
This graduate course is designed to improve verbal and written communication skills of PhD students in statistical sciences. The students are expected to attend regularly (at least 80%) the weekly research seminars of the Department of Statistical Sciences, other relevant seminars such as the Fields Distinguished Lecture Series in Statistical Sciences, as well as special seminars or workshops organized by the department on communication skills. Students are expected to select two research talks and submit a written report on each of them. The reports are supposed to be concise (23 pages; 10001500 words), summarizing the talks, and critiquing the research and/or presentations if possible. Based on their reports, the students in the class are expected to produce one 15minute presentation for the class. The presentation can either discuss both reports or only one of the reports in more detail. Evaluation:

F  L0101: R3:305:30  EP 409  Sun, Lei / Craiu, Radu  
Optimal transport is a vast subject and has deep connections with analysis, probability and geometry. In recent years optimal transport has found widespread applications in data science (a notable example is the Wasserstein GAN). In this course we offer a balanced treatment featuring both the theory and applications of the subject. After laying down the theoretical foundation including the Kantorovich duality, we turn to numerical methods and their applications to data science. Possible topics include entropic regularization, dynamic formulations, gradient flows, statistical divergences and the WGAN. Our main reference is the recent book Computational Optimal Transport by Gabriel Peyré and Marco Cuturi. Prerequisite:
Course credit: 0.25 FCE 
F – 1st half (Sept. 13th – Oct. 18th)  L0101: F912  SS 1085  Wong, Leonard  
The course will introduce students to the basic theory of stochastic optimal control. We will cover both the analytic approach, including an introduction to viscosity solution theory, and the probabilistic approach which is based on BSDE and the stochastic maximum principle. Applications to portfolio optimization and contract theory will be discussed. Prerequisite to this course include (measuretheoretic) probability theory and stochastic calculus. Prerequisite: (measuretheoretic) probability theory and stochastic calculus Course credit: 0.25 FCE 
F – 1st half (Sept. 10th – Oct. 15th)  L0101: T912  EP 409  Zhang, Yuchong  
Random matrix theory is now a big subject with applications in many disciplines of science, engineering and statistics. This course will cover fundamental concepts, principal and theory in random matrix theory, orienting towards the needs and interests in statistics. Applications to big data analytics and geometric data analysis are provided. Course credit: 0.5 FCE 
F – 1st half (Sept. 11th – Oct. 16th)  L0101: W101  MY 480  Sun, Qiang 
Winter Session 2020: Course Listings
Course 
Title (click for description) 
Session 
Section/Time 
Location 
Instructor 

(STA302H1)

Introduction to data analysis with a focus on regression. Initial Examination of data. Correlation. Simple and multiple regression models using least squares. Inference for regression parameters, confidence and prediction intervals. Diagnostics and remedial measures. Interactions and dummy variables. Variable selection. Least squares estimation and inference for nonlinear regression. Prerequisite: STA238H1/STA248H1/STA255H1/STA261H1/ECO227Y1 CSC108H1/CSC120H1/CSC121H1/CSC148H1 STA1002H 
S (J) 
L0101: T1012, R10
L0201: T23, R13

HS 610 (L0101);
ES 1050 (L0201);

Daignault, Katherine 
(STA303H1)

Analysis of variance for oneand twoway layouts, logistic regression, loglinear models, longitudinal data, introduction to time series. Prerequisite: STA1001H or equivalent 
S (J) 
L0101: TR1012;
L0201: T24, R111;
L0301: W36 (New Section!)

BA 1160 (L0101);
MP 203 (L0201);
BA 1160 (L0301)

Bolton, Liza (L0101, L0201)
Brown, Patrick (L0301)

(STA304H1)

Design of surveys, sources of bias, randomized response surveys. Techniques of sampling; stratification, clustering, unequal probability selection. Sampling inference, estimates of population mean and variances, ratio estimation., observational data; correlation vs. causation, missing data, sources of bias. Exclusion: STA322H1 Prerequisite: ECO220Y1/ECO227Y1/GGR270Y1 / PSY202H1/SOC300Y1/STA221H1/STA255H1/261H1/248H1 
S (J) 
L0101: M4, R35
L0201: W69

SF 1105 (L0201);

Tounkara, Fode 
(STA305H1)

This crosslisted course covers a number of topics used in the design and analysis of experiments. The course is intended for students of statistics as well as students of other disciplines (eg. engineering, experimental science, etc.) who will use experimental design and analysis in their work.The course will cover the following topics: randomization, blocking Latin squares, balanced incomplete block designs, factorial experiments, confounding and fractional replication, components of variance, orthogonal polynomials, response surface methods. Additional topics will be covered based on students’ interest as time permits. Prerequisite: STA302H/352Y/ECO327Y/ECO357Y or permission of instructor. 
S (J) 
L0101: M11, W111;
L0201: T1, R35

SueChee, Shivon  
(STA437H1)

Practical techniques for the analysis of multivariate data; fundamental methods of data reduction with an introduction to underlying distribution theory; basic estimation and hypothesis testing for multivariate means and variances; regression coefficients; principal components and the partial multiple and canonical cor relations; multivariate analysis of variance; classification and the linear discriminant function. The use of R software should be expected. Prerequisite: STA302H/352Y Recommended Preparation: MAT223H/240H 
S (J)  L5101: M123, W2  NF 003(M), OI G162(W)  SueChee, Shivon 
(STA447H1)

Discrete and continuous time processes with an emphasis on Markov, Gaussian and renewal processes. Martingales and further limit theorems. A variety of applications taken from some of the following areas are discussed in the context of stochastic modeling: Information Theory, Quantum Mechanics, Statistical Analyses of Stochastic Processes, Population Growth Models, Reliability, Queuing Models, Stochastic Calculus, Simulation (Monte Carlo Methods). Prerequisite: STA347H or equivalent knowledge of probability theory; and MAT235Y/237Y or equivalent knowledge of multivariate calculus and basic real analysis. 
S (J)  L5101: R69  MB 128  Rosenthal, Jeffrey 
(STA465H1)

Data acquisition trends in the environmental, physical and health sciences are increasingly spatial in character and novel in the sense that modern sophisticated methods are required for analysis. This course will cover different types of random spatial processes and how to incorporate them into mixed effects models for Normal and nonNormal data. Students will be trained in a variety of advanced techniques for analyzing complex spatial data and, upon completion, will be able to undertake a variety of analyses on spatially dependent data, understand which methods are appropriate for various research questions, and interpret and convey results in the light of the original questions posed. 
S (J)  L0101: R1, F111  SS 2110  Simpson, Daniel 
(STA414H1)

This course will consider topics in statistics that have played a role in the development of techniques for data mining and machine learning. We will cover linear methods for regression and classification, nonparametric regression and classification methods, generalized additive models, aspects of model inference and model selection, model averaging and tree bassed methods. Prerequisite: either STA302H or CSC411H 
S (J) 
L0101: M25;
L5101: T710

EM 001 (L0101);
SS 2117 (L5101)

Duvenaud, David (L0101)
Leo, Justin (L5101)

The course will focus on generalized linear models (GLM) and related methods, such as generalized additive model involving nonparametric regression, generalized estimating equations (GEE) and generalized linear mixed models (GLMM) for longitudinal data. This course is designed for Master and PhD students in Statistics, and is REQUIRED for the Applied paper of the PhD Comprehensive Exams in Statistics. We deal with a class of statistical models that generalizes classical linear models to include many other models that have been found useful in statistical analysis, especially in biomedical applications. The course is a mixture of theory and applications and includes computer projects featuring R (S+) or/and SAS programming. Topics: Brief review of likelihood theory, fundamental theory of generalized linear models, iterated weighted least squares, binary data and logistic regression, epidemiological study designs, counts data and loglinear models, models with constant coefficient of variation, quasilikelihood, generalized additive models involving nonparametric smoothing, generalized estimating equations (GEE) and generalized linear mixed models (GLMM) for longitudinal data. Prerequisite: Advanced Calculus, Linear Algebra, STA 347 and STA 422 (upperdivision courses on probability and statistical inference) or equivalent, STA 302 (linear regression), Statistical Computing using R (S+) or/and SAS (alternative softwares are allowed). However, please be advised that I may not be familiar with the software of your choice resulting in limited assistance. 
S (J)  L0101: W25  BL 325  Alexander, Monica  
(STA457H1)

An overview of methods and problems in the analysis of time series data. Topics include: descriptive methods, filtering and adjustment, spectral estimation, bivariate time series models. The course will cover the following topics:
Further topics, time permitting: multivariate models; GARCH models; statespace models 
S (J) 
L0101: R 36;
L5101: T69 (New section!)

NF 003 (L0101);
MS 3154 (L5101)


STA 2211H is a followup course to STA 2111F, designed for Master’s and Ph.D. level students in statistics, mathematics, and other departments, who are interested in a rigorous, mathematical treatment of probability theory using measure theory. Specific topics to be covered include: weak convergence, characteristic functions, central limit theorems, the RadonNykodym Theorem, Lebesgue Decomposition, conditional probability and expectation, martingales, and Kolmogorov’s Existence Theorem. 
S (J)  L0101: R 912  BA 1230  Volgushev, Stanislav  
(STA453H1)

This course is a continuation of STA2112 and designed for graduate students in statistics and biostatistics. Topics include:
Prerequisite: STA2112 
S  L0101: W1012, F10  Brenner, David  
Overview This course is designed to provide graduate students with experience in statistical consulting. Students are active participants in research projects brought to the Statistical Consulting Service (SCS) of the Department of Statistics. The course is offered over the two sessions, fall (SeptemberDecember) and winter (JanuaryApril). The overall workload is approximately equivalent to a half graduate course and students receive a half credit. Students are not expected to have had any experience as consultants. The purpose of the course is to provide this experience so that graduates will be better able to function in such an environment when they have completed the course. The course also provides students with the opportunity to become familiar with statistical software packages such as The SAS System. There is supervision and assistance to novice consultants. Content There is some classroom instruction at the start of the term, and meetings occasionally are called to discuss special topics and for students to compare experiences. Students serve as apprentice statisticians and work under the guidance of the instructor and the SCS Coordinator on individual projects. Projects are assigned to students as they come in to the SCS. There are periods of inactivity when there are no projects and other times are very busy. The pattern of work is more like that associated with a business or working environment than a traditional course. While some consideration is taken of other academic demands on students, those enrolling must be aware that work on projects may require precedence at times. Evaluation Students will be graded on the quality of their work as stati stical consultants. This involves the ability to do work in a timely fashion, the quality of advice provided and the quality of the presentation of advice and written work to clients. Prerequisite: Students should have taken some applied statistics courses such as an undergraduate regression course. Also undergraduate courses in applied statistics, sample survey, design of experiments and time series analysis are recommended but these are not required. Also taking some of the other 2000 level applied statistics courses is recommended as this course will serve as an excellent opportunity to put the content of these courses to work. 
Y  L0101: T1012  IN 209  Taback, Nathan  
(ACT466H1)

Limited fluctuation or American credibility, on a full and partial basis. Greatest accuracy or European credibility, predictive distributions and the Bayesian premium, credibility premiums including the Buhlmann and BuhlmannStraub models, empirical Bayes nonparametric and semiparametric parameter estimation. Simulation, random numbers, discrete and continuous random variable generation, discrete event simulation, statistical analysis of simulated data and validation techniques. Prerequisite: Consult the instructor concerning necessary background for this course 
S  L0101: T11, R1012  SS 2119  Broverman, Sam 
Please note that STA3000Y F & S can only be taken by PhD students in the Department of Statistical Sciences. This is the Department’s core graduate course in statistical theory. It covers the basic principles of statistical inference, their application to a variety of statistical models, and some generalizations to more complex settings. Prerequisite:

S  L0101: M101  HS 696  Evans, Mike  
Please note: students need permission from instructor to join this course! This course focuses on advanced theory and modeling of financial derivatives. The topics include, but are not limited to: HJM interest rate models, LFM and LSM market models; foreign exchange options; defaultable bonds; credit default swaps, equity default swaps and collateralized debt obligations; intensity and structural based models; jump processes and stochastic volatility; commodity models. As well, students are required to complete a project, write a report and present a topic of current research interest. Prerequisite: STA 2503 or equivalent knowledge. 
S  L5101: M69  HS 618  Zhang, Yuchong  
STA4502H 
This course will focus on convergence rates and other mathematical properties of Markov chains on both discrete and general state spaces. Specific methods to be covered will include coupling, minorization conditions, spectral analysis, and more. Applications will be made to card shuffling and to MCMC algorithms. Course credit: 0.25 FCE 
S  2nd half (Fab. 26th – April 1st)  L0101: W111  SS 2101  Rosenthal, Jeffrey 
STA4505H 
With the availability of high frequency financial data, new areas of research in stochastic modeling and stochastic control have opened up. This 6 week course will introduce students to the basic concepts, questions and methods that arise in this domain. We will begin with the classical market microstructure models, understand different theories of price formation and price discovery, identify different types of market participants, and then move on to reduced form models. Next, we will investigate some of the typical algorithmic trading strategies employed in industry for different asset classes. Finally, we will develop stochastic optimal control problems for solving optimal liquidation and high frequency market making problems and demonstrate how to solve those problems using the principles of dynamic programming leading to HamiltonJacobiBellman equations. Students will also have a chance to work with historical limit order book data, develop Monte Carlo simulations and gain a working knowledge of the models and methods. Tentative topics include: Market Microstructure Overview of Stochastic Calculus Dynamic Programming & HJB Dynamics of LOB Optimal Liquidation Market Making Risk Measures Course credit: 0.25 FCE 
S  2nd half (Fab. 25th – Mar. 30th)  L5101: T69  EP 409  Jaimungal, Sebastian 
STA4512H 
The general mathematics and logical foundations for statistical inference: geometric, algebraic and topological symmetries that arise naturally in the solution to the inference problem, including rigorous comparison of the bayesian and frequentist approaches, and the group theoretic considerations of invariance (algebraic and logical symmetry), both on the sample space as well as on the parameter space (and both either implicit or manifest) that must be taken into account in the analysis. Unusual for the development, but fundamental to the inherent logic of such considerations, the finitefinite case is given special attention in respect of both sample space and parameter space. Course credit: 0.25 FCE 
S  2nd half (Fab. 24th – Mar. 30th)  L0101: M123  LM 158  Brenner, David 
STA4518H 
This course will give an overview of robust statistical methods, that is, methods that are insensitive to outliers or other data contamination. Topics will include theoretical notions such as qualitative robustness and breakdown point, robust estimation of location (minimax variance and bias) and scale parameters, robust estimation in regression and multivariate analysis, and applications (including in computer vision). Prerequisite: STA2112H permission Course credit: 0.25 FCE 
S  2nd half (Fab. 25th – Mar. 30th)  L0101: T14  SS1088  Knight, Keith 
JAS1101H 
This graduatelevel course provides an introduction to the crossdisciplinary field of astrostatistics, and is intended for both astronomy and statistics students. We will cover topics in statistics (e.g., hierarchical Bayesian analysis, time series analysis, and cluster analysis) in the context of their applications to astronomical research (e.g., studies of galaxies, the Milky Way, exoplanets, and stellar populations). These topics will be covered through two main aspects of the course: 1) peerinstruction and collaboration on a term project, and 2) readings, inclass discussion, and exercises related to current astrostats literature. For the term project, the students will develop practical skills by collaborating in crossdisciplinary teams on a research project in astrostatistics using real astronomical data. 
S  L0101: M9:3011:30, W910:30  AB 113  Eadie, Gwendolyn 