These data have the potential to shed light on the genetic factors influencing traits and diseases, as well as on questions of ancestry and population history. Fundamentals of Computational Biology: Models and Inference. 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. Algorithms for Sequential Estimation. Particle Filters. Prerequisite(s): STAT 24500 and STAT 34300, or some background in analysis and previous exposure to stochastic processes. *Sources for school statistics and data include the U.S. Department of Education's National Center for Education Statistics and the Integrated Postsecondary Education Data System. Course description is subject to change. Prerequisite(s): STAT 30100 and STAT 30400 and STAT 31015, or consent of instructor. This class primarily concerns the design and analysis of Monte Carlo sampling techniques for the estimation of averages with respect to high dimensional probability distributions. Basic empirical process tools will also be discussed. 100 Units. 100 Units. Prerequisite(s): Graduate student in the Physical Sciences Division or consent of instructor. This course is only open to graduate students in Statistics, Applied Mathematics, and Financial Mathematics, and to undergraduate Statistics majors, or by consent of instructor. 100 Units. The focus is on theory and practice of linear models, including the analysis of variance, regression, correlation, and some multivariate analysis. In addition to the courses, seminars, and programs in the Department of Statistics, courses and workshops of direct interest to statisticians occur throughout the University, most notably in the programs in statistics and econometrics in the Booth School of Business and in the research programs in Health Studies, Human Genetics, Financial Mathematics and Econometrics, Computer Science, Economics and NORC (formerly the National Opinion Research Center). The field of statistics has become a core component of research in the biological, physical, and social sciences, as well as in traditional computer science domains such as artificial intelligence. The two central models to be discussed are Liouville quantum gravity which arises from exponentiating a two-dimensional Gaussian free field, as well as uniform infinite planar triangulation/quadrangulation. Time Dependent Data. Students enrolled in 200 units are considered half-time. Further Mathematical Computation: Matrix Computation and Optimization. Instructor(s): D. Hedeker Terms Offered: Spring Advanced Statistical Inference II. Equivalent Course(s): FINM 33170. Welcome to the Department of Statistics at the University of Chicago. STAT 36700. We will be much less interested in algebraic results that follow from axiomatic definitions of fields and vector spaces but much more interested in analytic results that hold only over the real and complex fields. The core of the course will include computation with topological spaces, the mapper algorithm, and persistent homology, and cover theoretical results, algorithms, and a variety of applications. Prerequisite(s): Consent of instructor. A rich series of interdisciplinary workshops and conferences bring together students and faculty from throughout the university for intellectual exchange. This class provides an introduction to Bayesian Inverse Problems and Data Assimilation, emphasizing the theoretical and algorithmic inter-relations between both subjects. 100 Units. Computational Imaging: Theory and Methods. For visa-related reasons, international student budgets may vary. To help with developing an intuitive understanding of the underlying mathematics and hand-on numerical skills, examples and exercises on PC are included. 5. We will start with a light and comparative introduction of two causal inference languages: the potential outcome model and the graphical representation of causal effects. 300.00 Units. The University of Chicago (UChicago, U of C, or Chicago) is a private research university in Chicago, Illinois.Founded in 1890, its main campus is located in Chicago's Hyde Park neighborhood. STAT 31440. Terms Offered: Spring Indeed, getting a method to work well for a particular problem may in itself be a research project. Prerequisite(s): STAT 24500 or STAT 24510 Theoretical derivations will be presented with emphasis on motivations, applications, and hands-on data analysis. With some additional statistical background (which can be acquired after the course), the participants will be able to read articles in the area. Prerequisite(s): STAT 30100 or STAT 30400 or STAT 31015, or consent of instructor. Instructor(s): Staff Terms Offered: Not offered in 2020-2021. Some knowledge of PDE and Fourier transforms is recommended. Modern Methods in Applied Statistics. Adv. Statistical Computing B. Introduction to learned emulators: how do they work, where have they been successful so far and what are the goals in this field? them but some theoretical concepts of the underlying problems will also be We will learn tangent spaces, efficient score functions, and information operators. STAT 31240. Basic concepts in probability are covered. Multiple Testing, Modern Inference, and Replicability. High-dimensional data is now common in many applications across the biological, physical, and social sciences. Equivalent Course(s): FINM 34510. This course will provide an introduction to the principles and methods for the analysis of longitudinal data. Topics include comparison of Bayesian and frequentist hypothesis testing; admissibility of Bayes' rules; confidence and credible sets; likelihood ratio tests and their asymptotics; Bayes factors; methods for assessing predictions for normal means; shrinkage and thresholding methods; sparsity; shrinkage as an example of empirical Bayes; multiple testing and false discovery rates; Bayesian approach to multiple testing; sparse linear regressions (subset selection and LASSO, proof of estimation errors for LASSO, Bayesian perspective of sparse regressions); and Bayesian model averaging. Prerequisite(s): STAT 343 (or a similar-level linear regression course) or consent of instructor; comfortable with programming in R. STAT 34800. STAT 31220. STAT 36600. The class will explore applications of these methods in Bayesian statistics and machine learning as well as to other simulation problems arising in the physical and biological sciences. The class will begin with an introduction to the numerical simulation of continuous time Markov processes including the discretization of stochastic (and ordinary) differential equations. Students may count either STAT 24400 or STAT 24410, but not both, toward the forty-two credits required for graduation. Prerequisite(s): Students should be familiar with a numerical programming language like Python, Julia, R, or Matlab and the content of CMSC 35400. We focus on the period from 1650 to 1950, with an emphasis on the mathematical developments in the theory of probability and how they came to be used in the sciences. We will study Gaussian approximations and optimization and sampling algorithms, including a variety of Kalman-based and particle filters as well as Markov chain Monte Carlo schemes designed for high-dimensional inverse problems. 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). program are a sequence of at least nine approved courses plus a Master's paper. Observability. 100 Units. STAT 30750. Contact information can be found under the listings of graduate programs on the Graduate Admissions website. Prerequisite(s): STAT 24400 or STAT 24410 or STAT 25100 or STAT 25150 Students are expected to analyze many real data sets. Familiarity with regression and with coding in R are recommended. modern statistics and applied mathematics. Almost all departmental activities–classes, seminars, computation, and student and faculty offices–are located in Jones Laboratory. http://boothportal.chicagobooth.edu/portal/server.pt/community STAT 31150. STAT 31450. The second half of the class will focus on individualized data analysis projects. A substantial fraction of available courses are the same as for the Ph.D. degree. 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. Nonparametric Inference. STAT 41511. presented. Course website: Some basics of social networks including structure and analysis of large data sets using distributed computation application! Equivalent ) is recommended for students continuing to STAT 24510 of ODE due the. Strong emphasis on motivations, applications, classifications, cluster analysis, and biological sciences clustered data, estimation... 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