Courses in the Statistics GIDP Curriculum
GIDP-sponsored/supported Statistics courses for:
Fall 2010
Spring 2010
Fall 2009
Spring 2009
Fall 2008
Spring 2008
All courses with STAT designators STAT 563/MATH 563 -- Probability Theory (3 units)
Description: Random variables, expectation and integration, Borel-Cantelli lemmas, independence, sums of independent random variables, strong law of large numbers, convergence in distribution, central limit theorem, infinitely divsible distributions
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): MATH 523B or MATH 527B or consent instructor.
Usually offered: Fall. STAT 564/MATH 564 -- Theory of Probability (3 units)
Description: Probability spaces, random variables, weak law of large numbers, central limit theorem, various discrete and continuous probability distributions. Graduate-level requirements include more extensive problem sets or advanced projects.
Grading: Regular grades are awarded for this course: A B C D E.
May be convened with: MATH 464.
Usually offered: Fall. STAT 566/MATH 566 -- Theory of Statistics (3 units)
Description: Sampling theory. Point estimation. Limiting distributions. Testing Hypotheses. Confidence intervals. Large sample methods. Graduate-level requirements include more extensive problem sets or advanced projects.
Grading: Regular grades are awarded for this course: A B C D E.
May be convened with: MATH 466.
Usually offered: Spring. STAT 567A/MATH 567A -- Theoretical Statistics (3 units)
Description: [Taught Spring semester in even-numbered years] Basic decision theory. Bayes' rules for estimation. Admissibility and completeness. The minimax theorem. Sufficiency. Exponential families of distributions. Complete sufficient statistics. Invariant decision problems. Location and scale parameters. Theory of nonparametric statistics. Hypothesis testing. Neyman-Pearson lemma. UMP and UMPU tests. Two-sided tests. Two-sample tests. Confidence sets. Multiple decision problems.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): MATH 466.
Usually offered: Spring. STAT 567B/MATH 567B -- Theoretical Statistics (3 units)
Description: [Taught Fall semester in even-numbered years] Basic decision theory. Bayes' rules for estimation. Admissibility and completeness. The minimax theorem. Sufficiency. Exponential families of distributions. Complete sufficient statistics. Invariant decision problems. Location and scale parameters. Theory of nonparametric statistics. Hypothesis testing. Neyman-Pearson lemma. UMP and UMPU tests. Two-sided tests. Two-sample tests. Confidence sets. Multiple decision problems.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): STAT 567A/MATH 567A.
Usually offered: Fall. STAT 571A/MATH 571A -- Advanced Statistical Regression Analysis (3 units)
Description: Regression analysis including simple linear regression and multiple linear regression. Matrix formulation and analysis of variance for regression models. Residual analysis, transformations, regression diagnostics, multicollinearity, variable selection techniques, and response surfaces. Students will be expected to utilize standard statistical software packages for computational purposes.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): MATH 410 or MATH 413, or equivalent; and MATH 461 or MATH 466, or equivalent.
Usually offered: Fall. STAT 571B/MATH 571B -- Design of Experiments (3 units)
Description: Principles of designing experiments. Randomization, block designs, factorial experiments, analysis of contrasts, multiple comparisons, analysis of variance and covariance, repeated measures, variance components analysis. Students will be expected to utilize standard statistical software packages for computational purposes.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): MATH 223, or equivalent; and STAT 571A/MATH 571A.
Usually offered: Spring. STAT 574B/ECON 574B -- Bayesian Statistical Theory and Applications (3 units)
Description: Basic theory of Bayesian inference, including analytical and numerical methods for assessing posterior and predictive distributions, and applications. Topics will include Bayesian analysis of normal linear regression and computational methods including Markov chain Monte Carlo.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): ECON 522A, ECON 522B, concurrent registration; or STAT 566/MATH 566 and STAT 571A/MATH 571A.
Usually offered: Spring. STAT 574C/SOC 574C -- Categorical data analysis (3 units)
Description: Analysis of contingency tables. Generalized Linear Models including logistic regression and log-linear models. Matched-pair models. Repeated categorical responses. Students will be expected to utilize standard statistical software packages for computational purposes.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): STAT 571A/MATH 571A or equivalent.
Usually offered: Spring. STAT 574E/MATH 574E/CPH 574E -- Environmental Statistics (3 units)
Description: [Taught Spring semesters in alternate years.] Statistical methods for environmental and ecological sciences, including nonlinear regression, generalized linear models, temporal analyses, spatial analyses/kriging, quantitative risk assessment.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): STAT 571B/MATH 571B, or PSYC 507C, or equivalent.
Usually offered: Spring. STAT 574G/GEOG 574G/MATH 574G -- Introduction to Geostatistics (3 units)
Description: Exploratory spatial data analysis, random function models for spatial data, estimation and modeling of variograms and covariances, ordinary and universal kriging estimators and equations, regularization of variograms, estimation of spatial averages, non-linear estimators, includes use of geostatistical software. Application of hydrology, soil science, ecology, geography and related fields.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): linear algebra, basic course in probability and statistics, familiarity with DOS/Windows, UNIX.
Usually offered: Fall. STAT 574S -- Survey Sampling (3 units)
Description: Techniques of statistical sampling in finite populations with applications in the analysis of sample survey data. Topics include simple random sampling for means and proportions, stratified sampling, cluster sampling, ratio estimates, and two-stage sampling.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): MATH 509C, or equivalent.
Usually offered: Fall. STAT 574T/MATH 574T -- Time Series Analysis (3 units)
Description: Methods for analysis of time series data. Time domain techniques. ARIMA models. Estimation of process mean and autocovariance. Model fitting. Forecasting methods. Missing data. Students will be expected to utilize standard statistical software packages for computational purposes.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): MATH 509C, or equivalent.
Usually offered: Fall. STAT 599 -- Independent Study (1-6 units)
Description: Qualified students working on an individual basis with professors who have agreed to supervise such work. Graduate students doing independent work which cannot be classified as actual research will register for credit under course number 599.
Grading: Alternative grades are awarded for this course: S P C D E.
Prerequisite(s): Student must submit an Independent Study Proposal Form in advance to the GIDP Office.
May be repeated: for a total of 6 units of credit.
Usually offered: Fall, Spring, Summer. STAT 675 -- Statistical Computing (3 units)
Description: Techniques of advanced computational statistics. Numerical optimization and integration pertinent for statistical calculations; simulation and Monte Carlo methods including Markov chain Monte Carlo (McMC); bootstrapping; smoothing/density estimation; and other modern topics.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): STAT 566/MATH 566, or equivalent, and knowledge of a computer programming language such as R, FORTRAN, or C.
Usually offered: pending. STAT 687/CPH 687 -- Theory of Linear Models (3 units)
Description: Theory of linear models including full-rank models and less than full rank fixed effects models. Topics will include distributional properties of quadratic forms, estimation methods, tests of hypothesis and confidence intervals as well as an introduction to computational aspects.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): STAT 566/MATH 566.
Usually offered: Fall. STAT 688/ABE 688/CPH 688 -- Statistical Consulting (3 units)
Description: The goal of this course is to teach statistics students to be effective statistical consultants. This is an advanced course in the selection and use of tools and statistical methods to analyze and interpret scientific, business and medical studies. This course will provide students with the ability to effectively and accurately acquire and convey information in verbal and written presentations.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): STAT 571A/MATH 571A; STAT 571B/MATH 571B, or equivalent; Working knowledge of Stata, SAS, SPSS, or R (or equivalent). Preferred: Probability & Math Statistics sequence (STAT 564/MATH 564 & STAT 566/MATH 566).
Usually offered: Fall. STAT 900 -- Research (1-3 units)
Description: Individual research, not related to thesis or dissertation preparation, by graduate students.
Grading: Alternative grades are awarded for this course: S P C D E K.
May be repeated: for a total of 6 units of credit.
Usually offered: Fall, Spring, Summer. STAT 910 -- Thesis (1-6 units)
Description: Research for the master's thesis (whether library research, laboratory or field observation or research, or thesis writing). Maximum total credit permitted varies with the major department or program.
Grading: Alternative grades are awarded for this course: S P E K.
May be repeated: for a total of 6 units of credit.
Usually offered: Fall, Spring, Summer. STAT 920 -- Dissertation (1-9 units)
Description: Research for the doctoral dissertation (whether library research, laboratory or field observation or research, or dissertation writing).
Grading: Alternative grades are awarded for this course: S P E K.
May be repeated: for a total of 36 units of credit.
Usually offered: Fall, Spring, Summer.
Other courses in the Statistics graduate curriculum A ME 574 -- Reliability and Quality Analysis (3 units)
Description: Probability and statistics with applications to reliability engineering, discrete and continuous statistical models for engineering variables, fundamentals of statistics, extreme value distribution, uniform distribution, reliability of systems operating at various stress levels, load sharing reliability. Graduate-level requirements include additional assignments and independent study, Monte Carlo simulation.
Grading: Regular grades are awarded for this course: A B C D E.
May be convened with: A ME 474.
Usually offered: Fall. AN S 513/EPID 513/GENE 513 -- Statistical Genetics for Quantitative Measures (3 units)
Description: This course provide the student with the statistical tools to describe variation in quantitative traits, particularly the decomposition of variation into genetic, environmental, and gene by environment interaction components. Convariance (resemblance) between relatives and heritability will be discussed, along with the topics of epistasis, oligogenic and polygenic traits, complex segregation analysis, methods of mapping quantitative trait loci (QTL), and estimation procedures. Microarrays have multiple uses, each of which will be discussed and the corresponding statistical analyses described.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): A course on basic genetic principles such as AN S 213, GENE 433, GENE 533, or GENE 545 and a current course on basic statistical principles such as GENE 509C or MATH 509C. A course in linear models such as STAT 571A/MATH 571A and a course in statistical inference.
Usually offered: Fall. AREC 559 -- Advanced Applied Econometrics (4 units)
Description: Emphasis in the course is on econometric model specification, estimation, inference, forecasting, and simulation. Applications with actual data and modeling techniques are emphasized.
Grading: Regular grades are awarded for this course: A B C D E.
Course includes 1 or more field trips.
Prerequisite(s): AREC 517, ECON 518, ECON 549.
Typical structure: 3 hours lecture, 1 hour discussion.
Usually offered: Fall. CPH 576B/EPID 576B -- Biostatistics for Research (3 units)
Description: Descriptive statistics and statistical inference relevant to biomedical research, including data analysis, regression and correlation analysis, analysis of variance, survival analysis, biological assay, statistical methods for epidemiology and statistical evaluation of clinical literature.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): CPH 576A, CPH 573A.
Usually offered: Spring. CPH 576C/EPID 576C -- Applied Biostatistic[al] Analysis (3 units)
Description: Integrate methods in biostatistics (EPID 576A, B) and Epidemiology (EPID 573A, B) to develop analytical skills in an epidemiological project setting.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): EPID 576A, EPID 576B, EPID 573A, EPID 573B or consent of instructor.
May be repeated: for credit 1 time (maximum 2 enrollments).
Usually offered: Summer. CPH 576D/EPID 576D -- Data Management and the SAS Programming Language (3 units)
Description: This course will introduce students to the fundamentals of data management using the SAS programming language. Emphasis will be placed on data manipulation, including reading, rocessing, recoding, and reformatting data. The approach will be to teach by example, with an emphasis on hands-on learning.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): EPID 576A, EPID 573A.
Usually offered: Summer. CPH 648/EPID 648 -- Analysis of High Dimensional Data (3 units)
Description: This course deals with the analysis of high dimensional data. It will cover multiple comparisons, clustering and classification of high dimensional data, and regression methods involving high dimensional variables. Students will also learn corresponding computer software.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): EPID 576A, EPID 576B, one year of college calculus, and a course in matrix algebra; or consent of instructor.
Usually offered: Spring. CPH 675/EPID 675 -- Clinical Trials and Intervention Studies (3 units)
Description: A fundamentals course on issues in the design, operation and analysis of controlled clinical trials and intervention studies. Emphasis on randomized long-term multicenter trials.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): EPID 576A, EPID 576B.
Usually offered: Spring. CPH 684/EPID 684 -- General Linear and Mixed Effects Models (3 units)
Description: This course introduces basic concepts of linear algebra that are essential for understanding more advance statistical modeling methodology. This knowledge is used to understand the General Linear Model (GLM) which includes ordinary linear regression, ANOVA, and other special applications and modern methods for the analysis of repeated measures, correlated outcomes and longitudinal data, including the unbalanced and incomplete data sets characteristic of biomedical research. Topics include an introduction to matrices for statistics, general linear models, analysis of correlated data, random effects models, and generalized linear mixed models.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): EPID 576A and EPID 576B.
Usually offered: Spring. CPH 685/EPID 685 -- Statistical Consulting (3 units)
Description: An advanced course in the application of biostatistical methods to analyze and interpret epidemiology, public health and medical studies. The goal is to assist students in becoming independent statistical consultants, able to effectively and accurately convey information in verbal and written presentations.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): EPID 576A, EPID 576B, EPID 576C, EPID 573A, EPID 576D suggested.
Usually offered: Fall. CPH 686/EPID 686 -- Survival Analysis (3 units)
Description: This course introduces basic concepts and methods for analyzing survival time data obtained from following individuals until occurrence of an event or their loss to follow up. We will begin this course from describing the characteristics of survival data and building the link between distribution, survival and hazard functions. After that we will cover non-parametric, semi-parametric and parametric models and two-sample test techniques. In addition we will also demonstrate mathematical and graphical methods for evaluation goodness of fit and introduce the concept of dependent censoring/competing risk. During the class students will also learn how to use a computer package, SAS, Splus or Stata to analyze survival data.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): EPID 576A, EPID 576B.
Usually offered: Spring. CPH 687/EPID 687* -- Theory of Linear Models (3 units)
Description: Theory of linear models including full-rank models and less than full rank fixed effects models. Topics will include distributional properties of quadratic forms, estimation methods, tests of hypothesis and confidence intervals as well as an introduction to computational aspects.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): STAT 566/MATH 566; Linear Algebra (at the level of MATH 413) helpful but not required.
Usually offered: Fall.
*Course under development ECE 631 -- Neural Networks (3 units)
Description: Theory and application of parallel distributed computation via elementary processing elements; PE models and neural analogies; statistical classification, supervised/unsupervised; neural net models; associative memories; training algorithms.
Grading: Regular grades are awarded for this course: A B C D E.
Usually offered: Fall. ECE 639 -- Detection and Estimation in Engineering Systems (3 units)
Description: Communication, detection and estimation as statistical inference problems. Optimal detection in the presence of Gaussian noise. Extraction of signals in noise via MAP and MMSE techniques.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): ECE 503.
Usually offered: Spring. ECOL 518 -- Spatio-Temporal Ecology (2 units)
Description: Population growth and species interactions in spatially and temporally varying environments. Meta populations and communities. The scale transition, the storage effect, nonlinear competitive variance, fitness-density covariance, disturbance, competition-colonization tradeoffs. Graduate-level requirements include the additional challenge of including less assistive text, as these students are expected to possess a broader knowledge base.
Grading: Regular grades are awarded for this course: A B C D E.
May be convened with: ECOL 418.
Usually offered: Fall. ECOL 581 -- Advanced Topics in Biological Statistics (3 units)
Description: Advanced topics in statistical methodology relevant to Biology, Genetics and Ecology. Maximum likelihood, General Linear models, randomization methods, power, distribution theory.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): basic course in statistics and/or matrix algebra.
Usually offered: Spring. ECON 518/AREC 518 -- Introduction to Econometrics (3 units)
Description: Statistical methods in estimating and testing economic models; single and simultaneous equation estimation, identification, forecasting, and problems caused by violating classical regression model assumptions. Graduate-level requirements include a research project that involves applications of econometric methods to the estimating and testing of behavioral models or simulation studies of the statistical properties of an econometric estimation technique. Advanced degree credit available for non-majors only.
Grading: Regular grades are awarded for this course: A B C D E.
May be convened with: ECON 418.
Usually offered: Fall, Spring. ECON 522A -- Econometrics (3 units)
Description: The theory of econometric estimation of single and simultaneous equation models.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): ECON 520.
Usually offered: Fall. ECON 522B -- Econometrics (3 units)
Description: Additional topics in the theory of econometric estimation of single and simultaneous equation models.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): ECON 522A.
Usually offered: Spring. ECON 549/AREC 549 -- Applied Econometric Analysis (3 units)
Description: Econometric model-building, estimation, forecasting and simulation for problems in agricultural and resource economics. Applications with actual data and models emphasized.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): ECON 518.
Usually offered: Spring. ED P 548 -- Statistical Packages in Research (4 units)
Description: Covers SPSS and SAS; creating data files; writing syntax; understanding documentation and output. Descriptive statistics, chi-square test of independence, regression, ANOVA.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): ED P 541 or equivalent.
Usually offered: Fall, Spring, Summer. ED P 558 -- Educational Tests and Measurements (3 units)
Description: Theoretical and practical application of psychometric techniques to test construction, analysis, and interpretation of test results.
Grading: Regular grades are awarded for this course: A B C D E.
Usually offered: Spring. ED P 646A -- Multivariate Methods in Educational Research (3 units)
Description: Multivariate statistical procedures, including multiple-regression variations, canonical correlation, discriminant analysis, multivariate analysis of variance/covariance and repeated measures.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): ED P 548; ED P 640 or equivalent.
Usually offered: Fall, Spring, Summer. ED P 658A -- Theory of Measurement (3 units)
Description: Advanced topics in theoretical and practical issues in psychometrics. Classical test theory including generalizability theory.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): ED P 548, ED P 558; Prerequisite or Concurrent registration, ED P 640.
Usually offered: Fall, Spring, Summer. ED P 658B -- Theory of Measurement (3 units)
Description: Advanced topics in theoretical and practical issues in psychometrics. Item response theory, scaling, and computer-adaptive testing.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): ED P 548, ED P 558, ED P 640. ED P 658A is not prerequisite to ED P 658B.
Usually offered: Fall, Spring, Summer. FSHD 617A -- Advanced Data Analysis: Structural Equation Modeling (3 units)
Description: This course covers basic and intermediate topics of confirmatory factor analysis and structural equation modeling (SEM). Students will learn the conceptual and mathematical bases of SEM; develop the ability to formulate and evaluate models; become proficient in using Lisrel; and apply these skills to research.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): FSHD 537A, FSHD 537B.
Usually offered: Fall. FSHD 617B -- Advanced Data Analysis: Dyadic Data Analysis (3 units)
Description: This course covers analysis of dyadic and small-group data. Students will learn the conceptual and mathematical bases of these approaches; formulate and evaluate models of interdependence; gain experience in writing results of interdependent data analyses; and apply these skills to research.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): FSHD 537A, FSHD 537B.
Usually offered: Spring. FSHD 617C -- Advanced Data Analysis: Multilevel Modeling (3 units)
Description: This course provides an introduction to Multilevel Modeling (MLM) and its implementation using SAS PROC MIXED. MLM is used for analyzing clustered data, such as longitudinal data (multiple observations nested within individuals) or data arising from couples or families (individuals nested within families).
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): FSHD 537A, FSHD 537B.
Usually offered: Fall. GEOG 657 -- Spatial Statistics and Spatial Econometrics (3 units)
Description: Formal analysis and modeling of spatial structures and processes; conceptual evaluation of point patterns, networks, surfaces and interaction.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): GEOG 557.
Identical to: PLNN 657.
Usually offered: Spring. GEOS 585A -- Applied Time Series Analysis (1-3 units)
Description: [Taught alternate years beginning Spring 2005]. Analysis tools in the time and frequency domains are introduced in the context of sample data sets drawn from ecology, hydrology, climatology and paleoclimatology. Students optionally use their own data in assignments applying methods.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): an undergraduate statistics course.
Typical structure: 2 hours lecture, 1 hour workshop.
Usually offered: Spring. HWR 655/C E 655 -- Stochastic Methods in Surface Hydrology (3 units)
Description: Topics and applications will vary with instructor. Advanced application of statistics and probability to hydrology, time series analysis and synthesis, and artificial neural network methods, as applied in the modeling of hydro-climatic sequences or Bayesian and other analyses in the decision making process of water resources. A combination of theory and application to the fields of hydrology, environmental and water resources engineering, climatic modeling, and other related natural resource modeling.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): HWR 545 or consult with course instructor.
May be repeated: for credit 1 time (maximum 2 enrollments).
Usually offered: Fall. LAW 611C -- Litigating with Experts/ECON 538 -- Law and Economics (3 units)
Description: This course is intended for graduate students interested in leading an independent statistical analysis of a problem, honing their communications skills, and learning about the litigation system in the United States. Students who plan on working with individuals who are not experts in statistical methods - policymakers, lawyers, etc. - will find this course useful preparation for learning to communicate statistical concepts in a non-technical manner; it is particularly useful for students who may go on to consulting or other related fields.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): One semester of graduate statistics or econometrics.
Usually offered: Spring. LING 539 -- Statistical Natural Language Processing (3 units)
Description: This course introduces the key concepts underlying statistical natural language processing. Students will learn a variety of techniques for the computational modeling of natural language, including: n-gram models, smoothing, Hidden Markov models, Bayesian Inference, Expectation Maximization, Viterbi, Inside-Outside Algorithm for Probabilistic Context-Free Grammars, and higher-order language models Graduate-level requirements include assignments of greater scope than undergraduate assignments. In addition to being more in-depth, graduate assignments are typically longer and additional readings are required.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): LING 538.
May be convened with: LING 439.
Usually offered: Fall, Spring. LING 582 -- Advanced Statistical Natural Language Processing (3 units)
Description: This course focuses on statistical approaches to pattern classification and applications of natural language processing to real-world problems
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): LING 539.
Usually offered: Fall. MATH 523A -- Real Analysis (3 units)
Description: Lebesgue measure and integration, differentiation, Radon-Nikodym theorem, Lp spaces, applications.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): MATH 425A.
Usually offered: Fall. MATH 565A -- Stochastic Processes (3 units)
Description: [Taught Spring semester in odd-numbered years]. Stochastic Processes in continuous time: Levy processes, Martingales, Markov processes, introduction to stochastic integrals.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): strong probability background.
Usually offered: Spring. MATH 565B -- Stochastic Processes (3 units)
Description: [Taught Fall semester in even-number years]. Stochastic processes in continous time; Levy processes, martingales, Markov processes, introduction to stochastic integrals.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): MATH 565A.
Usually offered: Fall. MATH 565C -- Stochastic Differential Equations (3 units)
Description: [Taught Spring semester in even-numbered years] Brownian motion, stochastic integrals, Ito formula, stochastic differential equations, diffusions, applications including: Partial differential equations, filtering, stochastic control
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): MATH 565B, MATH 468/568 or consent of instructor.
Usually offered: Spring. MATH 568 -- Applied Stochastic Processes (3 units)
Description: Applications of Gaussian and Markov processes and renewal theory; Wiener and Poisson processes, queues. Graduate-level requirements include more extensive problem sets or advanced projects.
Grading: Regular grades are awarded for this course: A B C D E.
May be convened with: MATH 468.
Usually offered: Spring. MATH 575A/C SC 575A -- Numerical Analysis (3 units)
Description: Error analysis, solution of linear systems and nonlinear equations, eigenvalue interpolation and approximation, numerical integration, initial and boundary value problems for ordinary differential equations, optimization.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): MATH 475B or MATH 456.
Usually offered: Fall. MATH 579 -- Game Theory and Mathematical Programming (3 units)
Description: [Taught Spring semester in even-numbered years] Linear inequalities, games of strategy, minimax theorem, optimal strategies, duality theorems, simplex method, nonzero sum games, applications to economics and decision making, Nash theorems. Graduate-level requirements include more extensive problem sets or advanced projects.
Grading: Regular grades are awarded for this course: A B C D E.
Identical to: C SC 579.
May be convened with: MATH 479.
Usually offered: Spring. MCB 516/ECOL 516 -- Bioinformatics and Genomic Analysis (3 units)
Description: Analysis of genome sequences for function using local and internet computer resources. Consult instructor for appropriate prerequisites before enrolling. Graduate-level requirement include a research project, written report, and a class presentation.
Grading: Regular grades are awarded for this course: A B C D E.
May be convened with: ECOL 416.
Usually offered: Fall. MGMT 582D -- Multivariate Analysis in Management (3 units)
Description: Analysis of variance and covariance, principal components, discriminant analysis, canonical correlation.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): MGMT 552. MGMT 582C is not prerequisite to MGMT 582D.
Usually offered: Spring. OPTI 528 -- Information and Noise in Quantum Optics and Photonics (3 units)
Description: Course introduces the mathematical methods used to handle stochastic processes and noise in quantum optics and photonics. The concept of information is introduced from a statistical point of view, leading to a discussion of the foundations of information theory.
Grading: Regular grades are awarded for this course: A B C D E.
Usually offered: Spring. OPTI 637 -- Principles of Image Science (3 units)
Description: Mathematical description of imaging systems and noise; introduction to inverse problems; introduction to statistical decision theory; prior information; image reconstruction and radon transform; image quality; applications in medical imaging; other imaging systems.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): OPTI 508, OPTI 512R, OPTI 604.
Usually offered: Spring. PHCL 595B/BME 595B/CBIO 595B/NRSC 595B/PCOL 595B/PS 595B -- Scientific Writing Strategies, Skills and Ethics (2 units)
Description: Provide students with skills to write/communicate effectively for a variety of scientific audiences; including scientific journals, funding institutions, potential employers as well as administration in academia and industry.
Grading: Regular or alternative grades can be awarded for this course: A B C D E or S P C D E.
Usually offered: Fall. PHYS 528 -- Statistical Mechanics (3 units)
Description: Physical statistics; the connection between the thermodynamic properties of a macroscopic system and the statistics of the fundamental components; Maxwell-Boltzmann, Fermi-Dirac, Einstein-Bose statistics.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): PHYS 476.
Usually offered: Fall. PSYC 507B -- Statistical Methods in Psychological Research (3 units)
Description: Statistical research design, methods and metascience. Application of the structural equations modeling to manifest variable (path analysis) and latent variable (multivariate) causal analysis, confirmatory and exploratory factor analysis, and hierarchical (variance component) linear models, including generalizability theory, meta-analytic, and growth curve parameter models.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): PSYC 507A.
Usually offered: Spring. PSYC 507C -- Research Design & Analysis of Variance (3 units)
Description: This course provides an overview of research design and statistical analysis with a special focus on Analysis of Variance. Various designs including between subjects, repeated measures, mixed, hierarchical and Latin Square designs are covered. Other topics addressed are contrasts among means and trends analysis.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): PSYC 507A.
Usually offered: Fall, Spring. PSYC 597G -- Graphical Exploratory Data Analysis (3 units)
Description: Explores graphical methods for displaying and understanding data. Topics include displaying data, robust descriptive measures, re-expressing or transforming data, understanding residuals, time-series and growth curves, and using graphical methods in conjunction with hypothesis testing. Enrollees will explore a data set of their own throughout the course.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): PSYC 507A.
Usually offered: Fall, Spring. RNR 520/GEOG 520 -- Advanced Geographic Information Systems (3 units)
Description: Examines various areas of advanced GIS applications such as dynamic segmentation, surface modeling, spatial statistics, and network modeling. The use of high performance workstations will be emphasized. Graduate-level requirements include a more extensive project and report.
Grading: Regular grades are awarded for this course: A B C D E.
May be convened with: GEOG 420.
Usually offered: Spring. SIE 520 -- Stochastic Modeling I (3 units)
Description: Modeling of stochastic processes from an applied viewpoint. Markov chains in discrete and continuous time, renewal theory, applications to engineering processes.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): SIE 321.
Usually offered: Fall. SIE 522 -- Engineering Decision Making Under Uncertainty (3 units)
Description: Application of principles of probability and statistics to the design and control of engineering systems in a random or uncertain environment. Emphasis is placed on Bayesian decision analysis. Graduate-level requirements include a semester research project.
Grading: Regular grades are awarded for this course: A B C D E.
Credit for: 1 unit engineering science, 2 units engineering design.
May be convened with: SIE 422.
Usually offered: Fall. SIE 525 -- Queuing Theory (3 units)
Description: Application of the theory of stochastic processes to queuing phenomena; introduction to semi-Markov processes; steady-state analysis of birth-death, Markovian, and general single- and multiple-channel queuing systems.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): SIE 520.
Usually offered: Spring. SIE 531 -- Simulation Modeling and Analysis (3 units)
Description: Discrete event simulation, model development, statistical design and analysis of simulation experiments, variance reduction, random variate generation, Monte Carlo simulation. Graduate-level requirements include a library research report.
Grading: Regular grades are awarded for this course: A B C D E.
Credit for: 1.5 units engineering science, 1.5 units engineering design.
May be convened with: SIE 431.
Usually offered: Fall, Spring. SIE 543 -- Game Theory (3 units)
Description: Principles of game theory. Historical context, Nash equilibrium, normal form and extensive forms. Stackelberg equilibrium, subgame perfect equilibrium. Cooperative games: core, bargaining, MCDM, social choice, Bayesian games. Examples from engineering, economics, military, national security, and environmental protection. Graduate-level requirements include more advanced homework, exams and projects.
Grading: Regular grades are awarded for this course: A B C D E.
May be convened with: SIE 443.
Usually offered: Fall. SIE 545 -- Fundamentals of Optimization (3 units)
Description: Unconstrained and constrained optimization problems from a numerical standpoint. Topics include variable metric methods, optimality conditions, quadratic programming, penalty and barrier function methods, interior point methods, successive quadratic programming methods.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): SIE 340.
Usually offered: Spring. SIE 606 -- Advanced Quality Engineering (3 units)
Description: Advanced techniques for statistical quality assurance, including multivariate control charting, principal components analysis, economic design of acceptance sampling plans and control charts, inspection errors, and select papers from the recent literature.
Grading: Regular grades are awarded for this course: A B C D E.
Prerequisite(s): SIE 530, SIE 506.
Usually offered: Spring. SOC 570B -- Social Statistics (3 units)
Description: Latent variable models, pooled cross-section models, event history models.
Grading: Regular grades are awarded for this course: A B C D E.
Usually offered: Spring. * Course under development
Note: Regular grades and Alternative grades are reviewed on the Universtiy registrar's website. | 
