| STAT 100 - Data, Models, and Reality: An Introduction to the Scientific Method |
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The formation of models of reality and the ways models are tested by their analysis in the light of data are considered. We cover a variety of examples from antiquity to the present time. |
| STAT 280 - Elemenatary Applied Statistics, Sections I & II |
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Topics include basic probability, descriptive statistics, probability distributions, confidence inervals, significance testing, simple linear regression and correlation, association between categorized variables. |
| STAT 281 - The Role of Chance in an Information World |
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The concept of probability is a powerful idea pervading many aspects of our lives and this notion has a surprisingly long history. This course is meant to be accessible to any curious undergraduate (or graduate student) and starts out with the colorful early history of the concept. Based on this background, during the next phase of course, we will develop the precise concepts that support the intuition. We move on to discussing the concept of probability and its use in an essential manner in the physics, computer science and mathematical finance. Mathematical background typical of a sophomore or junior in science, engineering, business or any field with a quantitative foundation, will be adequate.
Cross-listed with COMP 281 and ELEC 281 |
| STAT 300 - Model Building |
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* DISTRIBUTION COURSE: GROUP III Examples to illustrate mathematical formulation (modeling) of scientific problems, their solution and interpretation. Problems from engineering, epidemiology, economics, and other areas are covered. Real-world situations are emphasized. Satisfies statistics design criteria.
Prerequisite: Math 211 or permission of instructor. |
| STAT 305 - Introduction to Statistics for the Biosciences |
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An introduction to statistics for Biosciences with emphasis on statistical models and data analysis techniques. Computer-assisted data analysis, including biological examples, is explored in laboratory sessions.
Pre-requisites: MATH 101 and MATH 102 |
| STAT 310 - Probability and Statistics |
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Probability theory and the central concepts and methods of statistics including probability distributions, expectation, estimation, hypothesis testing, sampling distributions, linear models. Section 1 presents the general use in multiple disciplines; section 2 focuses on problem sets and examples in civil and environmental engineering.
Prerequisite: MATH 102 Recommended prerequisite: MATH 212
Cross-listed with: ECON 382 |
| STAT 312 - Probability & Statistics for Civil & Environmental Engineers (replaced section 2 of STAT 310) |
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Basic Methodologies and problem solving of probability and statistics for civial and environmental engineering. Calculus is required. |
| STAT 313 - Uncertainty & Risk in Urban Infrastructures |
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Practical applications and relevance of infrastructure risks are developed in the context of real engineering problems and phenomena, including unique systems and challenges of the gulf coast area. The course starts with a survey of the roles of probability in engineering and the focuses on computer-based methods, the Bayesian approach, risk analysis tools, and infrastructure safety.
Prerequisite: STAT 310, preferably section 2.
Cross-listed with: CEVE 313 |
| STAT 331 - Applied Probability |
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Elementary probability theory, conditional probability, independence, discrete and continuous random variables, expectation, standard discrete and continuous distributions, transform techniques, central limit theorems, estimation, and correlation. Selected topics such as the Poisson process, Markov chains, and statistical techniques. Illustrations from engineering are emphasized.
Pre-requisites: MATH 212
Cross-listed with: ELEC 331 |
| STAT 339 - Statistical Methods - Psychology (no longer jointed listed with PSYC) |
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Introduction to quantitative and computer methods applicable to the analysis of experimental and correlational data. |
| STAT 385 - Methods for Data Analysis and System Optimization |
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The three general topic areas covered in this methodology oriented course are statistical methods including regression, sampling and experimental design; simulation based methods in statistics, queueing and inventory problems; and an introduction to optimization methods. Excel will serve as the basic computing software.
Prerequisite(s): STAT 280 or STAT 305 or STAT 310 |
| STAT 400 - Econometrics |
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Survey of estimation and forecasting models. Includes multiple regression time series analysis. A good understanding of linear algebra is highly desirable.
Prerequisite(s): ECON 382 or STAT 310 or STAT 381 AND MATH 211 or MATH 355 or CAAM 335 or permission of instructor.
Cross-listed with: ECON 400 |
| STAT 405 - Statistical Computing and Graphics |
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Programming techniques and tools useful and advanced statistical studies. Higher level graphical methods and exploratory data analysis. |
| STAT 410 - Introduction to Regression and Statistical Computing |
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A survey of regression, linear models, and experimental design. Topics include simple and multiple linear regression, single- and multi-factor studies, analysis of variance, analysis of covariance, model selection, diagnostics. Data analysis using statistical software is emphasized.
Prerequisite(s): STAT 310 or STAT 331 or permission of instructor |
| STAT 411 - Advanced Statistical Method (previously STAT 420) |
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Advanced topics in statistical applications such as sampling, experimental design and statistical process control.
Prerequisite: STAT 310 |
| STAT 413 (now STAT 405) - Statistical Computing in Practice |
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Practical aspects of statistical computing, graphics and code development with exposure to multiple software packages, editing environments and hardware platforms. |
| STAT 420 (now STAT 411) - Statistical Process Control and Experimental Design |
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An historical development of quality control including the approaches of Ford, Pareto, Shewhart, Deming, Box and Tageuchi. Experimental designs include block studies, factorial and fractional factorial designs, crossed and nested factors, balanced designs. Special topics may include sample size determination, response surface methodology, and repeated measures.
Prerequisite: STAT 310 or STAT 331 |
| STAT 421 - Computational Finance II: Time Series Analysis |
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Applied time series modeling and forecasting, with applications to financial markets. UG/GR version: STAT 621. This the undergraduate version of STAT 621.
Prerequisite: STAT 310 or STAT 331 Recommended prerequisite: STAT 410 |
| STAT 422 - BAYESIAN DATA ANALYSIS |
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This course will cover Bayesian methods for analyzing data. The emphasis will be on applied data analysis rather than theoretical development. We will consider a variety of models, including linear regression, hierarchical models, and models for categorical data. Computational methods will be emphasized.
Equivalent course: STAT 622
Prerequisite: STAT 410 |
| STAT 423 - Probability in Bioinformatics & Genetics |
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Course introduces the student to modern biotechnology and genomic data. Statistical methods to analyze genomic data are covered, including probability models, basic stochastic processes, and statistical modeling. Biological topics include DNA sequence analysis, phylogenetic inference, gene finding, and molecular evolution.
Equivalent course: STAT 623
Pre-requisite(s): STAT 305 or STAT 310 or STAT 331 or permission of instructor |
| STAT 431 - Overview of Mathematical Statistics |
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Topics include random variables, distributions, transformations, moment generating functions, common families of distributions, independence, sampling distributions, the basics of estimation theory, hypothesis testing and Bayesian inference.
Prerequisites: STAT 310 or STAT 331 |
| STAT 440 - Statistics for Bioengineering |
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Course covers application of statistics to bioengineering. Topics include descriptive statistics, estimation, hypothesis testing, ANOVA, and regression. Offered first five weeks of the semester. See BIOE 440.
Prerequisite: CAAM 210
Cross-listed: BIOE 440 |
| STAT 450 - Practicum in Statistical Modeling (not offered every year) |
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This course introduces current theoretical and applied problems encountered in statistical practice. The content changes each semester in response to contemporary topics.
Equivalent course: STAT 540
Instructor permission required. |
| STAT 453 - Biostatistics |
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An overview of statistical methodologies useful in the practice of Biostatistics. Topics include epidemiology, rates, and proportions, categorical data analysis, regression, and logistic regression, retrospective studies, case-control studies, survival analysis. Real biomedical applications serve as context for evaluating assumptions of statistical methods and models. S-Plus (R) serves as computing software.
Equivalent course: STAT 553
Prerequisite: STAT 410 or permission of instructor |
| STAT 470 - From Sequence to Structure: An Introduction to Computational Biology |
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Contemporary introduction to problems in computational biology spanning sequence to structure. The course has three modules: the first introduces students to the design and statistical analysis of gene expression studies; the second covers statistical machine learning techniques for understanding experimental data generated in computational biology; and the third introduces problems in the modeling of protein structure using computational methods from robotics. The course is project oriented with an emphasis on computation and problem-solving.
Prerequisite: COMP 280 AND COMP 212 AND STAT 310 or STAT 331
Cross-listed with: BIOE 470, COMP 470 |
| STAT 484 - Environmental Risk Assessment & Human Health |
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This course is a series of group projects. Student assessment is performed through quantification of the role and contributions of each student in the group and the overall strength of the project. Undergraduates will be members of the teams led by graduate students. The grading scale for each group will be separate. Graduate students will be graded on leadership as well as the other aspects of the project. Undergraduates will be graded on their ability to contribute to the group effort. xlisted with CEVE 484. |
| STAT 485 - Quantitative Environmental Decision Making |
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A project oriented computer intensive course focusing on statistical and mathematical solutions and investigations for the purpose of environmental decisions. This course is the undergraduate version of STAT 685 with reduced requirements.
Equivalent course: STAT 685
Prerequisite: STAT 305 AND STAT 385 or permission of instructor |
| STAT 486 - Methods in Computational Finance I: Market Models |
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Standard computational finance courses blindly use tools based on the Efficient Market Hypothesis. Extensive data analysis shows that the EMH is seriously flawed as are the tools based upon it. This is a course based on the analysis of market data without pre-conceived assumptions.
Equivalent course: STAT 686
Prerequisites: STAT 310 or STAT 331 |
| STAT 490 - Independent Study |
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Independent Study |
| STAT 491 - Independent Study |
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Independent Study |
| STAT 495 - Introduction to Statistics (no longer offered) |
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This course is taught through the Political Science Department as POLI 495. |
| STAT 499 - Mathematical Sciences VIGRE Seminar |
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This course prepares a student for research in the mathematical sciences. Each section is dedicated to a different topic. Current topics include bioinformatics, biomathematics, computational finance, simulation driven optimization, and data simulation. Each semester may introduce new topics.
Equivalent course: STAT 699 |
| STAT 503 - Topics in Methods and Data Analysis |
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Applications of least squares and general linear mode.
Cross-listed with: POLI 503 |
| STAT 509 - Advanced Psychological Statistics I |
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Introduction to inferential statistics with emphasis on analysis of variance.
Cross-listed with: PSYC 502 |
| STAT 510 - Advanced Psychological Statistics II |
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A continuation of PSYC 502, focusing on multiple regression. Other multivariate techniques and distribution-free statistics are also covered.
Cross-listed with: PSYC 503 |
| STAT 522 - Advanced Bayesian Statistics |
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Modern topics in Bayesian statistics. |
| STAT 532 - Mathematical Statistics I |
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The first semester in a two-semester sequence in mathematical statistics: random variables, distributions, small and large sample theorems of hypothesis testing, point estimation, and confidence intervals; topics such as exponential families, univariate and multivariate linear models, and nonparametric inference will also be discussed. Required for graduate students in statistics.
Prerequisite(s): STAT 410 AND STAT 431 or permission of instructor |
| STAT 533 - Advanced Statistical Inference (previously Mathematical Statistics II ) |
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A continuation of STAT 532. Required for Ph.D. students in statistics.
Corequisite: STAT 581 Pre-requisites: STAT 532 |
| STAT 540 - Practicum in Statistical Modeling, no longer offered. Equivalent course STAT 606 |
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This course introduces current theoretical and applied problems encountered in statistical practice. The content changes each semester in response to contemporary topics. Designed for graduate students in statistics. Prerequisite - STAT 431 or consent of instructor. |
| STAT 541 - Multivariate Analysis |
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Study of multivariate data analysis and theory. Topics include normal theory, principal components, factor analysis, discrimination, estimation and hypothesis testing, multivariate analysis of variance and regression clustering. |
| STAT 542 - Simulation |
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Topics in stochastic simulation including; random number generators; Monte Carlo methods, resampling methods, Markov Chain Monte Carlo, importance sampling and simulation based estimation for stochastic processes. Pre-requisites: STAT 532 |
| STAT 545 - Generalized Linear Models & Categorical Analysis - this course is taught every other year |
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Contingency tables, association parameters, chi-squared tests, general theory of generalized linear models, logistics regression, loglinear models, poisson regression.
This course is taught on an every other year basis. |
| STAT 546 - Design and Analysis of Experiments and Sampling Theory |
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Graduate Level Course |
| STAT 547 - Survival Analysis |
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Lifetime tables, cumulative distribution theory, censored data, Kaplan-Meier survival curves, log-rank tests, Cox proportional hazards models, parametric and non parametric estimation, hypothesis testing. |
| STAT 549 - Functional Data & Wavelets |
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Statistical methods for functional data, with emphasis on non-parametric bases representations, such as wavelets. Topics include smoothing of noisy signals, non parametric estimation of densities and regression functions, and representations of stochastic processes. Some emphasis is given to Bayesian inferential procedures. Matlab software is used for class demonstrations. No prior knowledge on wavelets is required. |
| STAT 550 - Nonparametric Function Estimation - this course is taught every other year |
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Survey of topics in data analysis including data visualization, multivariate density estimation, and nonparametric regression. Advanced applications will include clustering, discrimination, dimension reduction, and bump-hunting using nonparametric density procedures.
This course is offered on an every other year basis. |
| STAT 551 - Advanced Topics in Time Series - this course is taught every other year |
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The course will cover current topics in both modeling and forecasting discrete and continuous time series. A brief coverage will also be given to spatial and spatial-temporal processes. Emphasis will be placed on applications in the area of computational finance.
Prerequisites - STAT 532, 421/621, or permission of instructor.
This course is offered on an every other year basis. |
| STAT 552 - Applied Stochastic Processes |
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This course covers the theory of some of the most frequently used stochastic processes in application; discrete and continuous time, Markov chains, Poisson and renewal processes, and Brownian motion.
Pre-requisites: STAT 431 |
| STAT 553 - Biostatistics |
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This course covers the design of biomedical and epidemiological studies and the analysis of the resulting data. The applied methods will be related to theory whenever practical. Emphasis will be placed on the similarity between various forms of analysis and reporting results in terms of measures of effect or association. Emphasis will also be given to identifying statistical assumptions and performing analyses to verify these assumptions. S-Plus (R) will serve as the basic computing software. Same as STAT 453 with advanced problem sets. Pre-requisite(s): STAT 410 or permission of instructor |
| STAT 581 - Mathematical Probability I |
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Measure-theoretic foundations of probability. Open to qualified undergraduates. Required for PhD students in Statistics.
Prerequisites: STAT 431 and STAT 552 or permission of instructor Also offered as CAAM 581. |
| STAT 582 - Mathematical Probability II |
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Measure-theoretic foundations of probability, A continuation of STAT 581. Pre-requisite(s): STAT 581 |
| STAT 583 - Introduction to Random Processes and Applications |
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Review of basic probability; Sequence of random variables; Random vectors and estimation; Basic concepts of random processes; Random processes in linear systems, expansion of random processes; Wiener filtering; Spectral representation of random processes; White-noise integrals.
This course is offered through the Electrical and Computer Engineering Department as ELEC 533. |
| STAT 586 - Wavelets and Spectral Analysis |
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| STAT 590 - Independent Study |
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| STAT 591 - Independent Study |
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Independent study for graduate level research topics in statistics. |
| STAT 600 - Graduate Seminar in Statistics |
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Students participate in the process of researching professional literature (journal articles, book chapters, dissertations), preparing, delivering and critiquing talks. Literature topics change each semester. Restriction(s): Must be enrolled in one of the following Major(s): Statistics. Must be enrolled in one of the following Level(s):Graduate. |
| STAT 601 - Graduate Seminar in Statistics |
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| STAT 604 - Advanced Economic Statistics |
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Statistical inference and the testing of hypotheses multiple and partial correlation analysis; analysis of variance and regression.
This course is offered through the Economics Department as ECON 504. |
| STAT 606 - SAS Statistical Programming, replaced STAT 540 Practicum in Statistical Modeling |
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This course will cover the following areas: 1) DATA step including arrays, merging, do-loop processing, if then else statements, SET statements, importing and exporting, space optimization 2) PROC TABULATE and PROC REPORT 3) Brief functions survey, e.g. random number generators, character and mathematical functions, time and date functions etc. 4) Formats 5) Brief survey of statistical PROC’s 6) SAS ODS (Output Delivery System) from statistical procedures 7) Output datasets from statistical procedures 8) PROC GRAPH and Statistical Graphics Procedures (SGPLOT, SGPANEL, SGSCATTER) 9) PROC SQL (includes built-in short course on basic SQL) 10) PROC IML including functions, subroutines and optimization etc. 11) Macro programming facility |
| STAT 610 - Econometrics I |
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Estimation and inference in single equation regression models, multicollinearity, autocorrelated and heteroskedastic disturbances, distributed lags, asymptotic theory, and maximum likelihood techniques. Emphasis is placed on the ability to analyze critically the literature. Also offered as ECON 510. Pre-requisite(s): ECON 504 |
| STAT 611 - Econometrics II |
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Topics in linear and nonlinear simultaneous equations estimation, including qualitative and categorical dependent variables models and duration analysis. Applied exercises use SAS and the Wharton Quarterly Econometric Model.
This course is offered through the Department of Economics as ECON 511. |
| STAT 620 - Special Topics |
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Seminar on advanced topics in Statistics. |
| STAT 621 - Computational Finance II: Time Series Analysis |
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Applied time series modeling and forecasting, with applications to financial markets with advanced problem sets. UG/GR version: STAT 421. This is the graduate version of STAT 421 with advanced assignments.
Pre-requisite(s): STAT 310 OR STAT 331 Recommended prerequisite(s): STAT 410 |
| STAT 622 - Bayesian Data Analysis |
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This course will cover Bayesian methods for analyzing data. The emphasis will be on applied data analysis rather than theoretical development. We will consider a variety of models, including linear regression, hierarchical models, and models for categorical data. Pre-requisites: STAT 410 |
| STAT 623 - Probability in Bioinformatics and Genetics |
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Advances in computers and biotechnology have had an immense impact on the biomedical fields, with broad consequences for humanity. Correspondingly, new areas of probability and statistics are being developed specifically to meet the needs of this area. This course also describes some of the main statistical applicaitons in the field, including gene findings and evolutionary inference.
Prerequisites: One of STAT 305, 310 or 331 or permission of instructor. |
| STAT 630 - Topics in Clinical Trials |
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This course deals with fundamental concepts in the design of clinical stuides, ranging from early dose-finding studies (phase I) to screening studies (phase II) to randomized comparative studies (phase III). The goal is to prepare the student to read the clinical trial literature critically and to design cilinical studies. Additionally, the faculty will introduce newer designs for clinical studies that incorporate prior knowledge and/or satisfy optimality considerations. Topics include protocol writing; randomization; sample size calculation; study design options; interim monitoring; adaptive designs; multiple end points; and writing up the results of a clinical trial for publication.
This course is not offered every year. |
| STAT 631 - Graphical Models |
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This course will focus on providing diverse mathematical tools for graduate students from statistical inference and learning; graph theory, signal processing and systems; coding theory and communications, and information theory. We will discuss exact and approximate statistical inference over large number of interacting variables, and develop probilistics and optimization-based computational methods. We will cover hidden Markov models, belief propagation, Monte Carlo sampling algorithms, and variational Bayesian methods.
Prerequisite: STAT 552 Cross-listed with: ELEC 633 |
| STAT 639 - Extreme Value Theory |
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Extreme Value Theory is used in many areas such as financial markets, risk management, environmental studies, as well as network design. In this course we will study the theory and practice of extreme value theory. Prerequisite(s): STAT 431, STAT 532, and 533 are not required but are recommended. |
| STAT 640 - Data Mining and Statistical Learning |
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Survey of ideas, methods, and tools for analyzing large data sets; techniques for searching for unexpected relationships in data. Topics from supervised and unsupervised learning include regression, discriminant analysis, kernels, model selection, bootstrapping, trees, MARS, boosting, classification, clustering, neural networks, SVM, association rules, principal curves, multidimensional scaling, and projection pursuit. |
| STAT 647 - Advanced Survival Analysis |
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Pre-requisites: STAT 547 |
| STAT 650 - Stochastic Differential Equations |
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This course will cover both theory and applications of stochastic differential equations. Topics include: the Langevin equation from physics, the Wiener process, white noise, the martingale theory, numerical methods and simulation, the Ito and Stratonovitch theories, applications in finance, signal processing, materials science, biology, and other fields.
Prerequisite(s): A course in stochastic processes and a graduate course in probability, or consent of instructor. |
| STAT 655 - Nonparametric Bayesian Data Analysis - this course is taught every other year |
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The course reviews the current state of nonparametric Bayesian inference problems, including density estimation, regression, survival analysis, hierarchical models and model validation.
Prereq- STAT 531, 420, or permission of instructor.
This course is offered on an every other year basis. |
| STAT 670 - Statistical Genetics |
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This course centers on applications of statistics in genetic problems, especially as they pertain to genotype-phenotype association. Various data structures will be the centerpiece of the course, including genotype, allele-sharing, and gene-expression. Topics include family and population-based study design, linkage, association, differential gene expression. Genetic analysis software will also be discussed and used. |
| STAT 675 - Advanced Methods Genomics & Proteomics |
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We propose to discuss development & application of statistical methods in the analysis of high-thorughput boinformatics data that arise from problems in medical research, in particular cancer research, molecular and sturctural biology. We present a broad overview of statistical inference problems related to three main high- throughput platforms: microarray gene expression, serial anaysis gene expression (SAGE), and mass spectrometry proteomic profiles. Our main focus is on the design, statistical inference and data analysis, from a statistician's perspective, of data sets arising from such high throughput experiments. |
| STAT 678 - Microarray Data Analysis |
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This course is an introduction to the statistical and bioinformatic analysis of microarray data. The course covers both Affymetrix oligonucleotide arrays and two- color fluorescence cDNA microarrays. The course introduces students to the full range of processing microarray experiments, from experimental design, through image processing, background correction, normalization, and quality control to the downstream statistical analysis of differential expression. The course includes coverage of the key statistical concept of multiple testing. The covers common methods of pattern identification and pattern recognition in the context of microarrays. It also includes the bioinformatic interpretation of the results through tools to interact with public genome databases. All concepts will be illustrated through hands-on interaction with publicly available microarray data sets. Homework assignments will require some knowledge of R, a statistical programming language. The course will include a brief introduction to R. This class meets in the GSBS library (BSRB 53.8351). |
| STAT 684 - Environmental Risk Assessment & Human Health |
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Learn and apply quantitative risk assessment methodology to estimate human health risk from environmental exposure to contamination in air, soil and water. Students will conduct a series of team projects focused on toxicology, risk based screening levels, exposure concentration estimation and risk characterization. xlisted with CEVE 684. |
| STAT 685 - Quantitative Environmental Decision Making |
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A project oriented computer intensive course focusing on statistical and mathematical solutions and investigations for the purpose of environmental decisions. This course is required for EADM students. Pre-requisite(s): STAT 305 Co-requisite(s): STAT 385 |
| STAT 686 - Comp Fin 1: Market Models |
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This course takes the classical efficient market models and superimposes upon it models for other stochastic phenomena not generally accounted for in efficient market theory, showing how risk is lessened by portfolios and other mechanisms. The course uses computer simulations as an altermative to closed form solutions with advanced problem sets. UG/GR version: STAT 486. Pre-requisite(s): STAT 310 OR STAT 331 |
| STAT 688 - Decision Theory with Medical Application |
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Statistical inference, decision theory, and simulation as applied to assist in making individual clinical decisions, policy recommendations, and as a guide to study design and research; topics include statistical decision theory, decision analysis, decision trees, markvo models and simulation, cost-effectiveness analysis, meta-analysis, and sensitivity analysis. Grading will be based on regularly assigned homework exercises and term projects.
Pre-requisites: STAT 422 AND STAT 410 or permission of instructor |
| STAT 699 - Mathematical Sciences VIGRE Seminar |
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This course prepares a student for research in the mathematical sciences on a specific topic. Each section is dedicated to a different topic. Current topics include bioinformatics, biomathematics, computational finance, simulation driven optimization, and data simulation. The topics change each semester. |
| STAT 800 - Thesis |
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Thesis for Graduate Students |