university of chicago graduate school statistics

Instructor(s): L. Lim     Terms Offered: Autumn Time Dependent Data. Participation in this course requires familiarity with pytorch and a strong background in statistical modeling. 100 Units. 100 Units. Examples will be drawn from mathematical modeling of physical and biological systems. Measure-Theoretic Probability I. All four of our master’s in business administration programs offer the same powerful MBA degree, the same world-class faculty, the same influential network, the same dynamic community.Only the format and the students’ professional profiles differ. physics, statistics, engineering, and finance). With a graduate degree, statisticians may find jobs working with data in many sectors, including business, government, academia, public health, technology and other science fields. 100 Units. Monte Carlo Simulation. Note(s): Recommended prerequisites: STAT 38300; or MATH 31200, MATH 31300, and MATH 31400; or consent of instructor. Stochastic Calculus. The University is in historic Hyde Park, a vibrant community with a rich campus life. Program Details. 100 Units. Graduate students in Statistics or Financial Mathematics can enroll without prerequisites. 100 Units. This course provides a transition between statistical theory and practice. Lower bound techniques such as Bayes, Le Cam, and Fano's methods will be taught. The large number of statistics related seminars is perhaps the best indication of the vibrancy of the statistics research community here at the University of Chicago. With some additional statistical background (which can be acquired after the course), the participants will be able to read articles in the area. Terms Offered: All quarters A short introduction to SAS will be given if time permits. Instructor(s): D. Hedeker     Terms Offered: Spring Terms Offered: Summer only The stochastic Taylor expansion provides the basis for the discrete-time numerical methods for differential equations. A substantial fraction of available courses are the same as for the Ph.D. degree. The students should have solid knowledge in at least two of the following areas: (1) Probability theory (either 31200-31300 or 38100-38300). Prerequisite(s): STAT 30200 or consent of instructor. Some knowledge of PDE and Fourier transforms is recommended. http://boothportal.chicagobooth.edu/portal/server.pt/community 100 Units. Introduction to Stochastic Processes I. In particular, it is one of the most fundamental mathematical tools used in financial mathematics (although we will not discuss finance in this course). STAT 31430. 100 Units. STAT 39800. With this foundation, we will proceed to discuss a variety of approaches to developing useful classes of Gaussian process models, with a focus on spatial-temporal processes. Smoothing. Instructor(s): R. Barber     Terms Offered: To be determined. 100 Units. 100 Units. Dynamic models discussed include vector autoregressive models, vector autoregressive moving-average models, multivariate regression models with time series errors, co-integration and error-correction models, state-space models, dynamic factor models, and multivariate volatility models such as BEKK, Dynamic conditional correlation, and copula-based models. STAT 30750. 5747 South Ellis Avenue The com) for recent UChicago grads is $64,000. Equivalent Course(s): STAT 24510. Gaussian processes are commonly used in statistical models for spatial and spatial-temporal processes and for computer model output. All sufficiently well-prepared students take 3 of 4 sequences in their first year: All students pass prelim exams in 2 of the 4 subjects by the beginning of their second year. This course gives an introduction to nonparametric inference, with a focus on density estimation, regression, confidence sets, orthogonal functions, random processes, and kernels. Students register for one of the listed faculty sections with prior consent from the respective instructor. STAT 37793. Terms Offered: Not offered in 2020-2021. Prerequisite(s): STAT 30100 or STAT 30400 or STAT 31015, or consent of instructor. 100 Units. STAT 41511. This course introduces the theory, methods, and applications of fitting and interpreting multiple regression models. Equivalent Course(s): CAAM 31450. Instructor(s): B. Chiu     Terms Offered: Winter Additional topics from algebraic topology, metric geometry, category theory, and quiver representation theory will be developed from applied and computational perspectives. 100 Units. One may view it as an "applied" version of Stat 30900 although it is not necessary to have taken Stat 30900; the only prerequisite for this course is basic linear algebra. STAT 42600. Terms Offered: Spring This course is for Statistics Master's students to carry out directed reading or guided work on topics related to their Master's papers. STAT 31240. Prerequisite(s): Either HGEN 47100 or both STAT 24400 and 24500. (3) Basic knowledge in game theory and algorithms. Equivalent Course(s): CMSC 35400, CAAM 37710. It continues to produce world-class mathematics research and is devoted to excellence in teaching. The course treats nonparametric methodology and its use, together with theory that explains the statistical properties of the methods. Basic empirical process tools will also be discussed. STAT 36350. This course will include lectures on the following topics: review of asymptotics for low dimensional time series analysis (linear and nonlinear processes; nonparametric methods; spectral and time domain approaches); covariance, precision, and spectral density matrix estimation for high dimensional time series; factor models; estimation of high dimensional vector autoregressive processes; prediction; and high dimensional central limit theorems under dependence. Terms Offered: Spring This course continues the development of Mathematical Statistics, with an emphasis on hypothesis testing. About. Terms Offered: Winter A typical nonparametric approach estimates a nonlinear function from an infinite dimensional space rather than a linear model from a finite dimensional space. 100 Units. STAT 70000. 100 Units. This course is primarily about iterative algorithms in matrix computation. Equivalent Course(s): CAAM 31140, CMSC 31140. This course will cover basic principles of computational imaging, including image denoising, regularization techniques, linear inverse problems and optimization-based solvers, and data acquisition models associated with tomography and interferometry. The main tools of stochastic calculus (Ito's formula, Feynman-Kac formula, Girsanov theorem, etc.) This course is part of a two-quarter sequence on the theory of statistics. Gene Regulation. The theoretical basis of the methods, the relation to linear algebra, and the effects of violations of assumptions are studied. Topics covered include metric spaces and basic topological notions, aspects of mathematical analysis in several variables, and an introduction to measure and integration. STAT 31190. This course is a prerequisite for "Advanced Topics in Causal Inference" and "Mediation, moderation, and spillover effects. Prerequisite(s): PBHS 30700 or PBHS 30900 or PBHS 30910 AND PBHS 32400 or applied statistics courses through multivariate regression. Instructor(s): C. Gao     Terms Offered: To be determined. The specific topics may include dynamic programming, algorithms for graphs, numerical optimization, finite-difference, schemes, matrix operations/factor analysis, and data management (e.g. Topics include continuous-time Markov chains, Markov chain Monte Carlo, discrete-time martingales, and Brownian motion and diffusions. 100 Units. Instructor(s): K. Wolter     Terms Offered: Autumn 300.00 Units. Topics include the examination of residuals, the transformation of data, strategies and criteria for the selection of a regression equation, nonlinear models, biases due to excluded variables and measurement error, and the use and interpretation of computer package regression programs. 5. Equivalent Course(s): HGEN 48600. Random Planar Geometry. STAT 30200. Students enrolled in the graduate level STAT 30750 will have additional work in assignments, exams, and projects including applications of matrix algebra in statistics and numerical computations implemented in Matlab or R. Some programming exercises will appear as optional work for students enrolled in the undergraduate level STAT 24300. Bayesian Nonparametrics. Theoretical Neuroscience: Statistics and Information Theory. The chief consideration in choosing a department at which to do graduate work in economics must be the quality of its faculty as economists and as teachers of economics. 100 Units. The Department of Economics at the University of Chicago has always ranked among the handful of leading departments in the world. Topics include standard distributions (i.e. The Office of the University Registrar is committed to supporting the university’s academic and administrative operations, as they relate to student success, by providing the data needed to make more informed decisions. Problems associated with multiple time scales will be discussed along with methods to address them (implicit discretizations, multiscale methods and dimensional reduction). This breadth of scholarship is valued at Chicago. Topics will include numerical linear algebra, optimization, graph theory, data analysis, and physical simulations. It enrolled 16,445 students in Fall 2019, including 6,286 undergraduates and 10,159 graduate students. Applied Analysis. Prerequisite(s): Multivariate calculus (MATH 15910 or MATH 16300 or MATH 16310 or MATH 19520 or MATH 20000 or MATH 20500 or MATH 20510 or MATH 20900 or PHYS 22100 or equivalent). The data analytic tools that we will study will go beyond linear and multiple regression and often fall under the heading of "Multivariate Analysis" in Statistics. Not offered in 2020-2021. STAT 30850. not offered in 2018-19 Canalization, a unifying biological principle first enunciated by Conrad Waddington in 1942, is an idea that has had tremendous intellectual influence on developmental biology, evolutionary biology, and mathematics. selected selected software packages. 100 Units. 100 Units. 4:00–5:00 pm Topics may include, but are not limited to, statistical problems in genetic association mapping, population genetics, integration of different types of genetic data, and genetic models for complex traits. Students may work with faculty from other departments; however, they still must obtain permission from and register with one of the listed faculty members in the Department of Statistics. 100 Units. Equivalent Course(s): STAT 24410. Stochastic Calculus I. Course content is subject to change in order to keep the contents up-to-date with new development in multivariate statistical techniques. Prerequisite(s): STAT 31220 Lastly, we will discuss algorithms for generalized and quadratic eigenvalue problems (QZ algorithm) as well as for singular value decomposition (Golub-Kahan and Golub-Reinsch). Digital revolutions, artificial intelligence, and new forms of management and governance all claim to be data-driven. Standard simulation tools such as importance sampling, Metropolis-Hastings, Langevin dynamics, and hybrid Monte Carlo will be introduced along with basic theoretical concepts regarding their convergence to equilibrium. STAT 31450. Instructor(s): M. Silber     Terms Offered: Spring 100 Units. Program requirement. The topics covered include: (1) stationary and unit-root non-stationary processes; (2) linear dynamic models, including Autoregressive Moving Average models; (3) model building and data analysis; (4) prediction and forecasting evaluation; (5) asymptotic theory for estimation including unit-root theory; (6) models for time varying volatility; (7) models for time varying correlation including Dynamic Conditional Correlation and time varying factor models. Prerequisite(s): Prior exposure to basic calculus and probability theory, CPNS 35500 or instructor consent. Proseminar in Probability. The course will focus on (1) formulating and understanding convex optimization problems and studying their properties; (2) understanding and using the dual; and (3) presenting and understanding optimization approaches, including interior point methods and first order methods for non-smooth problems. STAT 37792. Other students may enroll with consent of instructor. Instructor(s): M. Wang     Terms Offered: Spring This course will explore modern approaches to optimization, data augmentation, and domain shift for deep neural networks from both theoretical and empirical perspectives. 100 Units. 100 Units. STAT 37710. Introduction to learned emulators: how do they work, where have they been successful so far and what are the goals in this field? STAT 31610. 100 Units. The central topic is probability. The course starts with a quick introduction to martingales in discrete time, and then Brownian motion and the Ito integral are defined carefully. This course considers mathematical and numerical methods to approach electronic structure of materials through several hot-topic examples including topological insulators and incommensurate 2D materials in addition to classical systems such as periodic crystals. Prerequisite(s): STAT 30900/CMSC 37810 Prerequisite(s): STAT 24410 and linear algebra (MATH 19620 or MATH 20250 or STAT 24300 or PHYS 22100 or equivalent). Prerequisite(s): PhD student in Statistics or Math or Computational and Applied Mathematics or TTIC or MS student in Statistics or Computational and Applied Mathematics. This course will begin with an overview of the theory for Gaussian processes, with a focus on stationary processes and their associated spectral properties and how these relate to problems of spatial interpolation. A basic familiarity with R is needed, but no prior programming experience is required. Longitudinal data consist of multiple measures over time on a sample of individuals. The course will also introduce students to a variety of practical topics such as the use of remote resources, version control with git, commonly used libraries for scientific computing and data analysis, and using and contributing to open source and collaborative projects. Equivalent Course(s): CHDV 32702, PBHS 33500. Introduction to Stochastic Processes II. Applied Functional Analysis. Further topics on statistical learning for high dimensional data and complex structures include penalized regression models (LASSO, ridge, elastic net), sparse PCA, independent component analysis, Gaussian mixture model, Expectation-Maximization methods, and random forest. Equivalent Course(s): FINM 33180, CAAM 32940. STAT 30600. Chicago, IL 60637 Topics in Statistical Machine Learning" is a second graduate level course in machine learning, assuming students have had previous exposure to machine learning and statistical theory. degree, the other to the Doctorate of Philosophy (Ph.D.). This quarter emphasizes methods for estimation and inference developed in these areas, along with theoretical analysis of their properties. appropriate) in the problem sets which students will solve using MATLAB. Instructor(s): R. Willett     Terms Offered: Spring (not necessarily in Python). Additional topics may include diagnostic plots, bootstrapping, a critical comparison of Bayesian and frequentist inference, and the role of conditioning in statistical inference. The course will also cover analytical methods and tools for solving these PDEs; such as separation of variables, Fourier series and transforms, Sturm-Liouville theory, and Green's functions. Equivalent Course(s): CMSC 25025. The Rackham Graduate School works together with faculty in the schools and colleges of the University to provide more than 180 graduate degree programs and to sustain a dynamic intellectual climate within which graduate … Course website: Of the 98 graduate programs offered at University of Illinois at Chicago, 8 are offered online or through graduate distance education programs. The main objects of interest are real- or complex-valued matrices, which may come from differential operators, integral transforms, bilinear and quadratic forms, boundary and coboundary maps, Markov chains, correlations, DNA microarray measurements, movie ratings by viewers, friendship relations in social networks, etc. STAT 31200. We will consider both mathematical and computational approaches in contexts where there are both single and multiple deterministic limits. Specific topics include maximum likelihood estimation, posterior distributions, confidence and credible intervals, principles of hypothesis testing, likelihood ratio tests, multinomial distributions, and chi-square tests. Prerequisite(s): STAT 38100. STAT 41500-41600. On the math section, 50% of admitted students scored between 750 and 800, 25% scored below 750, and 25% scored a perfect 800. A rich series of interdisciplinary workshops and conferences bring together students and faculty from throughout the university for intellectual exchange. STAT 31060. The course material changes every year, and the course may be repeated for credit. The massive increase in the data acquired, through scientific measurement on one hand and through web-based collection on the other, makes the development of statistical analysis and prediction methodologies more relevant than ever. 100 Units. STAT 41520. Topics in Random Matrix Theory. 100 Units. STAT 37790. Topics in Causal Inference. topics covered are: 1. Review of optimization,linear algebra, probabilistic and calculus (MATH 16300 or MATH 16310 or MATH 19520 or MATH 20000 or MATH 20500 or MATH 20510 or MATH 20800). 100 Units. ,Lectures are oriented around specific examples from a variety of content areas. Techniques discussed are illustrated by examples involving both physical and social sciences data. Distribution Theory. Instructor(s): Staff     Terms Offered: Spring Welcome to the Department of Statistics at the University of Chicago. This course concerns the estimation of the dynamic properties of time-dependent stochastic systems. Instructor(s): Y. Amit     Terms Offered: Autumn 100 Units. Though many of these algorithms first arose in physical applications such as simulating the motion of stars or the propagation of light and sound, they have subsequently found many fruitful applications in signal processing and data science. Coursework consists of intensive graduate courses in probability, theoretical statistics, and statistical computing as well as an advanced course in modern applied statistics and a capstone course. Please visit the Booth portal and search via the course search tool for the most up to date information: Data may vary depending on school and academic year. Further Mathematical Computation: Matrix Computation and Optimization. Statistical Genetics. STAT 31220. Performing valid inference is challenging since we must find a way to condition on the outcome of the selection process which is not always simple to characterize. STAT 37810 recommended. This course is designed for graduate students and advanced undergraduate students from the social sciences, education, public health science, public policy, social service administration, and statistics who are involved in quantitative research and are interested in studying causality. This course considers the modeling and analysis of data that are ordered in time. Terms Offered: Winter Differential Equation Instructor(s): Y. Ji     Terms Offered: TBD A detailed set of regulations can be found here. Computations in class and for homework will be carried out in Matlab. ,of relevant statistical theory will be presented, emphasis is on the development of statistical solutions to interesting applied problems. presented. Equivalent Course(s): CAAM 31230. Instructor(s): S. Stigler     Terms Offered: Spring Note(s): STAT 26300 can count as either a List A or List B elective in the Statistics major. Program elective. STAT 38660. Other topics (e.g., methods for dependent observations) may be covered if time permits. There will be a strong emphasis on stochastic processes and inference in complex hierarchical statistical models. Equivalent Course(s): MATH 38309, CAAM 31100, CMSC 37812. Taking courses with potential advisers is part of this process. Terms Offered: Spring The course will use examples from real data (where He has a PhD in econometrics and statistics. The city of Chicago has been an incredible laboratory in which to study this history, and the University of Chicago has been a leader in doing just that.” Alyssa O'Connor, JD'16, Law School “I chose UChicago because I was looking for a tight–knit campus experience … This course is about using matrix computations to infer useful information from observed data. Stochastic Processes in Gene Regulation. Particle Filters. Mathematical Aspects of Electronic Structure of Materials. … Applied Linear Stat Methods. The first quarter introduces a range of statistical frameworks for finding low-dimensional structure in high-dimensional data, such as sparsity in regression, sparse graphical models, or low-rank structure. This course introduces stochastic processes not requiring measure theory. This is a research topic course on certain aspects of random planar geometry. Adjoint Methods include algorithms for clustering, binary classification, and hierarchical Bayesian modeling. Epidemiologic Methods. Students will learn to design, implement, and test code in Python. on algorithms for such problems, their properties, and computations involving In the Department of Statistics—among the top 5 of 65 statistics programs in the nation—the faculty involves students in the invention, study, and development of principles and methods for modeling uncertainty via mathematical probability; for designing experiments, surveys, and observational programs; and for analyzing and interpreting analytical data. Nonlinear and time-varying relationships are also discussed. 100 Units. 100 Units. This course includes broad views of the development of the subject and closer looks at specific people and investigations, including reanalyses of historical data. STAT 31410. Introduction to Causal Inference. The selection of topics is influenced by recent research results, and students can take the course in more than one quarter. Prerequisite(s): STAT 30900/CMSC 37810 or consent of instructor. Limited Memory Methods. Illinois at Chicago is a public graduate school in Chicago, Illinois. Instructor(s): Staff     Terms Offered: Autumn The course takes place in the second half of the autumn quarter, after STAT 37810 (Statistical Computing A). Applied Dynamical Systems. decoding. Programming will be based on Python and R, but previous exposure to these languages is not assumed. Forecasting plays an important role in business planning and decisionmaking. This course starts with a brief review of stochastic calculus and stochastic differential equations, then emphasizing the numerical methods needed to solve such equations. Note(s): Students who need it should take Linear Algebra (STAT 24300 or equivalent) concurrently. Topics covered will include an overview of tools from the calculus of variations and partial differential equations, applications to the design of numerical methods for image denoising, deblurring, and segmentation, and the study of convergence properties of the associated models. This course covers random sampling methods; stratification, cluster sampling, and ratio estimation; and methods for dealing with nonresponse and partial response. This course covers statistical decision theory with examples drawn from modern high-dimensional and nonparametric estimation. Indeed, getting a method to work well for a particular problem may in itself be a research project. At Berkeley, category theory, data analysis I with the instructors rich campus life artificial intelligence and... Then Brownian motion and diffusions and rigorously $ 64,000 programming projects ( using MATLAB and quiver theory. 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