Meets for the first half of the term only. The Engineering & Applied Science Librarian focuses on titles in mathematical and theoretical statistics. Then we move forward to the distributed setting, and develop Quantized Frank-Wolfe (QFW), a general The remaining course is fulfilled through the senior requirement. Statistics & Data Science MS Overview | Department of Statistics Statistics & Data Science MS Overview Program Overview The M.S. QRMW 1pm-2:15pm, S&DS352b / MB&B452b / MCDB452b, Biomedical Data Science, Mining and Modeling Mark Gerstein, Techniques in data mining and simulation applied to bioinformatics, the computational analysis of gene sequences, macromolecular structures, and functional genomics data on a large scale. Examples of courses that might be terrific courses but do not satisfy the requirements of the. Course crTTh 1pm-2:15pm, S&DS123b / CPSC123b / PLSC351b / S&DS523b, YData: An Introduction to Data Science Ethan Meyers, Computational, programming, and statistical skills are no longer optional in our increasingly data-driven world; these skills are essential for opening doors to manifold research and career opportunities. In this guide, you will discover resources at Yale that support statistics, data science and related fields. primarily for graduate students. The simulated tempering algorithm uses an auxiliary We are interested in a broad range of topics in . QRMW 2:30pm-3:45pm, S&DS241a / MATH241a, Probability Theory Yihong Wu, Introduction to probability theory. Applications chosen from communications, networking, image reconstruction, Bayesian statistics, finance, probabilistic analysis of algorithms, and genetics and evolution. Position Focus: Yale University Library (YUL) seeks user-centered, collaborative, and creative applicants for the position of Librarian for Political Science and . Prerequisite: level of S&DS241.TTh 11:35am-12:50pm, * S&DS425a or b, Statistical Case Studies Brian Macdonald, Statistical analysis of a variety of statistical problems using real data. Examples of such courses include: CPSC223, 323, 424, 437. If you are a Ph.D. student, you receive a fellowship that covers the full cost of tuition through at least your first five years. Essentials of probability, including conditional probability, random variables, distributions, law of large numbers, central limit theorem, and Markov chains. Advanced text-mining analysis in the R package tm and web-scraping algorithms in Python are applied to both archival and current data to enhance analysis. Computation & Machine Learning: one of S&DS 262, 265, 317, 355, 365, CPSC 223, CPSC 477, PHYS 378, PLSC 468. S&DS100 Students gain the necessary knowledge base and useful skills to tackle real-world data analysis challenges. Terminal M.S. Candidates must be pursuing an MS or PhD in one of the following areas: Electrical Engineering, Computer Science, Biomedical Engineering, Bioinformatics, Applied Mathematics, Statistics, or can demonstrate equivalent qualifications in related fields. Students who wish to major in Statistics and Data Science are encouraged to take S&DS220 or a 100-level course followed by S&DS230. Advanced students may substitute S&DS 351 or 364 or EENG 431. QRTTh 2:30pm-3:45pm, S&DS138a / AFST378a / EVST378a, Foreign Assistance to Sub-Saharan Africa: Archival Data Analysis Russell Barbour, This course reviews the many years of U.S. development assistance to Africa using archival data from the Agency for International Development (USAID), nonprofit organizations, and specialized agencies such as the U.S. Department of Agriculture and nineteen U.S. government agencies involved in development assistance to Africa. Course crTTh 1pm-2:15pm, S&DS109a, Introduction to Statistics: Fundamentals Jonathan Reuning-Scherer, General concepts and methods in statistics. (YData) is an introduction to data science that emphasizes developing skills, especially computational and programming skills, along with inferential thinking. QRHTBA, S&DS431a / AMTH431a, Optimization and Computation Yang Zhuoran, This course is designed for students in Statistics & Data Science who need to know about optimization and the essentials of numerical algorithm design and analysis. The overarching goal of the course is teach students how to design algorithms for Machine Learning and Data Analysis (in their own research). Yale's new Institute for Foundations of Data Science is accepting applications for. FALL term Title: The Power and Limitations of Convexity in Data Science, New statistical and computational phenomena from deep learning, Statistically Efficient Offline Reinforcement Learning and Causal Machine Learning, Department of Statistics and Data Science, Institute for Foundations of Data Science debuts with interdisciplinary vision. and estimation capabilities, have become increasingly popular in a considerable variety of application fields. communication-efficient distributed FW framework for both convex and non-convex objective functions. 1 probability and statistical theory course; 2 statistical methodology and data analysis courses; 1 computational and machine learning course; and 2 half-credit courses or 1 course in discipline area, as specified, Programs and Certificates in Yale College. Some knowledge of real analysis assumed. under which the original cause of slow convergence will persist. While the main purpose of some of these courses is not computing, students who have taken at least two of these courses will be capable of digesting and processing data. in Music, be sure to use the Graduate School of Arts and Sciences Ph.D./Master's . QRTTh 1pm-2:15pm, S&DS103a / EP&E209a / PLSC453a, Introduction to Statistics: Social Sciences Jonathan Reuning-Scherer, Descriptive and inferential statistics applied to analysis of data from the social sciences. We are committed to furthering education and research in the rapidly growing field of data science. 4 en route to a Ph.D., which is for students already enrolled in a Yale Ph.D. program. We study the task of generating samples from the "greedy'' gaussian mixture posterior. law and applied mathematics. Students gain the necessary knowledge base and useful skills to tackle real-world data analysis challenges. In the first chapter, a subsequence-based variational Bayesian inference This panel is a great opportunity to learn about positions in . Introduction to regression and linear models. flattens the posterior by reducing the size of the observed subsample. Privacy policy, Title: The Power and Limitations of Convexity in Data Science, Department of Statistics and Data Science. Prerequisite 1 term course from S&DS100, 101109, 123 or 220(or an introductory data analysis course in another department), Distribution of courses1 probability and statistical theory course; 2 statistical methodology and data analysis courses; 1 computational and machine learning course; and 2 half-credit courses or 1 course in discipline area, as specified, ProfessorsDonald Andrews, Andrew Barron, Jeffrey Brock, Joseph Chang, Katarzyna Chawarska, Xiaohong Chen, Nicholas Christakis, Ronald Coifman, James Duncan, John Emerson (Adjunct), Debra Fischer, Alan Gerber, Mark Gerstein, Anna Gilbert, John Hartigan (Emeritus), Edward Kaplan, Harlan Krumholz, John Lafferty, David Pollard (Emeritus), Nils Rudi, Jasjeet Sekhon, Donna Spiegelman, Daniel Spielman, Hemant Tagare, Van Vu, Heping Zhang, Hongyu Zhao, Harrison Zhou, Steven Zucker, Associate ProfessorsPeter Aronow, Forrest Crawford, Ethan Meyers (Visiting), Sekhar Tatikonda, Yihong Wu, Assistant ProfessorsElisa Celis, Zhou Fan, Joshua Kalla, Amin Karbasi, Roy Lederman, Vahideh Manshadi, Fredrik Savje, Zhuoran Yang, Ilker Yildirim. publications in Probability and Statistical TheoryOne from S&DS238, 240, 241, 242. SAS/R is used for analysis of data. This can be either of: Two of the 12-credit seminars (S&DS 170, 171 and 172) that accompanied S&DS 123 in Spring 2019. Practical statistical analysis also uses a variety of computational techniques, methods of visualizing and exploring data, methods of seeking and establishing structure and trends in data, and a mode of questioning and reasoning that quantifies uncertainty. Using the R language,topics include exploratory data analysis, probability, hypothesis testing, confidence intervals, regression, statistical modeling, and simulation. The suggested prerequisite for the certificate is an introductory course, selected from one of the following courses: S&DS100, 101109, 123 or 220, or an introductory data analysis course from another department. degree program The B.S. Work Week: Standard (M-F equal number of hours per day) Searchable Job Family: Library. S&DS Assistant Professor Roy Lederman receives 2023 Sloan Research Fellowship, an award that recognizes outstanding early-career Daniel Spielman, Sterling Professor of Computer Science, Statistics and Data Science, and Mathematics, is the inaugural James A. Attwood Director of the new institute. Full Time. In this guide, you will discover resources at Yale that support statistics, data science and related fields. Check out tuition fees, course rankings, entry requirements, application deadlines, and course reviews. S&DS100b, Introductory Statistics Ethan Meyers, An introduction to statistical reasoning. Many academic programs, such as Economics, Management, Political Science, Psychology, and Sociology use statistical methodologies in their teaching and research and are supported by the collections. Specifically, B.S. They should not be introductory statistics or probability courses within that discipline, nor should they be courses that focus on statistical methods for analyzing data that has already been cleaned. Practice in reading medical literature competently and critically, as well as practical experience performing statistical analysis of medical data. Topics include linear and nonlinear models, maximum likelihood, resampling methods, curve estimation, model selection, classification, and clustering. YData is an introduction to Data Science that emphasizes the development of these skills while providing opportunities for hands-on experience and practice. QRTTh 2:30pm-3:45pm, S&DS410a, Statistical Inference Zhou Fan, A systematic development of the mathematical theory of statistical inference covering methods of estimation, hypothesis testing, and confidence intervals. For more information, please see: Research Opportunities in Data Science and Fundamental Physics at Wright Lab. ), ( Topics include nonparametric regression and classification, kernel methods, risk bounds, nonparametric Bayesian approaches, graphical models, attention and language models, generative models, sparsity and manifolds, and reinforcement learning. Statistics and Data Science: Tables and Formulas Welcome Tutorials for STATA & R Tables and Formulas Managing Your Research Online Books CRC Standard Probability and Statistics Tables and Formulae by Daniel Zwillinger; Stephen M. Kokoska Call Number: Online Book Publication Date: 1999 This course is intended as a bridge between AP statistics and courses such as S&DS230, Data Exploration and Analysis. Yale CEA. in Statistics and Data Science is designed to acquaint students with fundamental techniques in the field. measurement error models are explored in three chapters. ECON136 may be substituted for S&DS242. They range from practical to theoretical. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. Contact Combined B.S./M.A. Prerequisites: Knowledge of linear algebra, multivariate calculus, and probability. temperature variable to flatten the target density (reducing the effective cluster separation). https://guides.library.yale.edu/statistics, Computational and Inferential Thinking: The Foundations of Data Science, Encyclopedia of Statistical Sciences (Wiley), Handbook Series Package: Handbook of Statistics [BSHOST], Handbook Series Package: Handbooks in Economics Series [BSHES], International Encyclopedia of the Social and Behavioral Sciences (Elsevier), 2nd edition. Lastly, we study the social implications of these decisions, and understand the legal, political and policy decisions that could be used to govern data-driven decision making by making them transparent and auditable. Sekhar Tatikonda and Daniel Spielman will serve as co-DUSes of the major. Librarian for Political Science and Statistics & Data Science. Workshop Calendar Essential Resources Computational and Inferential Thinking: The Foundations of Data Science It looks like you're using Internet Explorer 11 or older. Students who complete one of these courses should consider taking S&DS230. Statistical Methodology and Data Analysis: two of S&DS 230, 242, 312, 361, 363, PLSC 349. QRMW 11:35am-12:50pm, S&DS351b / EENG434b / MATH251b, Stochastic Processes Amin Karbasi, Introduction to the study of random processes including linear prediction and Kalman filtering, Poison counting process and renewal processes, Markov chains, branching processes, birth-death processes, Markov random fields, martingales, and random walks. o course may be used to fulfill more than one requirement of the certificate. degree program The B.A. ), One of the Data Science in a Discipline Area courses approved for the data science, ANTH 376 (Observing and Measuring Behavior), ASTR 255 (Research Methods in Astrophysics), ASTR 330 (Scientific Computing in Astrophysics), ASTR 356 (Astrostatistics and Data Mining), BENG 469 (Single-cell Biologies, Technologies, and Analysis), ECON 438 (Applied Econometrics: Politics, Sports, Microeconomics), GLBL 191 (Research Design and Survey Analysis), MB&B 452 / MCDB 452 / S&DS 352 (Biomedical Data Science, Mining and Modeling), PLSC 340 / S&DS 315 (Measuring Impact and Opinion Change), PLSC 341 / GLBL 195 (Logic of Randomized Experiments in Political Science), PLSC 438 (Applied Quantitative Research Design), PLSC 454 (Data Science for Politics and Policy), PSYC 235 (Research Methods in Psychology), PSYC 238 (Research Methods in Decision Making and Happiness), PSYC 258 / NSCI 258 (Computational Methods in Human Neuroscience), PSYC 438 / NSCI 441 (Computational Models of Human Behavior), S&DS 171 (YData: Text Data Science: An Introduction) if taken in Spring 2020 or later, S&DS 172 (YData: Data Science for Political Campaigns)if taken in Spring 2020 or later, S&DS 173 (YData: Analysis of Baseball Data) if taken in Spring 2020 or later, S&DS 174 (YData: Statistics in the Media), S&DS 177 (YData: Covid-19 Behavorial Impacts). For students with sufficient preparation in mathematics, S&DS238 meeting should inform. Statistics and Data Science can be taken either as a primary major or as one of two majors, in consultation with the DUS. Computers are used for calculations, simulations, and analysis of data. In this course, we explore how data science is being used to design winning campaigns. Associate Professor, Department of Computer Science and Economics Elisa Celis Assistant Professor of Statistics & Data Science Joseph Chang James A. Attwood Professor of Statistics and Data Science Xiaohong Chen Malcolm K. Brachman Professor of Economics Nicholas Christakis Sterling Professor of Social and Natural Science Alex Coppock . Seeking summer internships in: - private equity. Students learn how data are obtained, how reliable they are, how they are used, and the types of inferences that can be made from them. . While some learners may wish to study data science through a traditional on-campus degree program or an intensive "bootcamp" class or school, the cost of these options can add up quickly once tuition as well as the cost of books and transportation and sometimes even lodging are . Students pursuing the B.S. and M.S. Data Science in Context Students are encouraged to take courses that involve the study of data in application areas. Data Analysis in a Discipline AreaTwo half-credit courses or one full-credit course from those approved for this requirement and listed on the S&DS website. The courses currently approved for this purpose are: ECON 439 (Applied Econometrics: Macroeconomic and Finance Forecasting), EVST 290 (Geographic Information Systems), Were open to adding more courses to this list (to suggest a course, email, Courses in this category should expose students to how data is gathered and used within a discipline. The course assumes familiarity with the basic ideas and techniques in machine learning, for example as covered in S&DS265. An introduction to statistical decision theory. . Check out tuition fees, course rankings, entry requirements, application deadlines, and course reviews. Data Science and Analytics Computer Science and Engineering Business Health Care Design Engineering Statistics Mathematics Law Architecture View All. Students completing the B.S. Box 208290 New Haven, CT 06520-8290 Email: joseph.chang@yale.edu Office: 24 Hillhouse Ave., Room 211 Teaching Fall, 2022 S&DS 238a/538a: Probability and Bayesian Statistics Research Yale University Careers New Haven, CT. Students should consider S&DS 103 or both S&DS 108, 109. This course counts towards the Data Science certificate but not the Statistics and Data Science major. After S&DS242 and MATH222 or 225. The mathematical foundation of statistics lies in the theory of probability, which is applied to make inferences and decisions under uncertainty. likelihood components (referred to as internal annealing). Continuous Greedy, a derivative-free and projection-free algorithm, that maximizes a monotone continuous DR-submodular function degree program and B.S. Students require background in linear algebra, multivariable calculus, probability and programming. In 2015-2018 I was a postdoc in the Program in Applied and Computational Mathematics at Princeton University, working with Amit Singer. Statistics and Data Science can be taken either as a primary major or as one of two majors, in consultation with the DUS. Bachelor of Science [B.S] Statistics and Data Science. The R computing language and Web data sources are used. It's been a run of form made all the more impressive by the simultaneous juggling of a statistics and data science degree at Yale, but this very balancing act could help guide the. degree program requires eleven courses, ten of which are from the seven discipline areas described below: MATH222 or 225or MATH226 from Mathematical Foundations and Theory; two courses from Core Probability and Statistics; two courses that provide Computational Skills; two courses on Methods of Data Science; and three courses from any of the discipline areas subject to DUS approval. New Haven, CT 06511. The Attwood Statistics Resource Fund : a decade of impact, 2009-2019 ( Book ) Most widely held works by Yale University Scalable Projection-Free Optimization by Mingrui Zhang . RoadmapSee visual roadmap of the requirements. Sequence alignment, comparative genomics and phylogenetics, biological databases, geometric analysis of protein structure, molecular-dynamics simulation, biological networks, microarray normalization, and machine-learning approaches to data integration. Exam Scores: IELTS 7.0 | TOEFL 100 | PTE 70 | Duolingo 120. This program is provided on-campus and off-campus. INR 57 L/Yr USD 68,831 /Yr. Still, this approach is sensitive to the ordering of The Certificate in Data Science is designed for students majoring in disciplines other than Statistics & Data Science to acquire the knowledge to promote mature use of data analysis throughout society. QRTTh 9am-10:15am, S&DS363b, Multivariate Statistics for Social Sciences Jonathan Reuning-Scherer, Introduction to the analysis of multivariate data as applied to examples from the social sciences. Check out tuition fees, course rankings, entry requirements, application deadlines, and course reviews. Prerequisites: S&DS 541 and S&DS 542 or equivalent, or permission of the instructor. Apply Enrollment is limited; requires permission of the instructor. Performed literature review and aggregated data on BIV systems; greywater; and the climactic needs of Karachi, Pakistan . The third chapter focuses on an empirical and my work spans multiple disciplines including data science, machine learning, fairness in socio-technical systems and algorithm design. applied Bayesian methodological topics and empirical examples focusing on nonhomogeneous hidden Markov models (NHMMs) and No course may be applied to satisfy the requirements of both a major and the certificate. Director of undergraduate studies: Sekhar Tatikonda, Rm. works in The Department of Statistics at the University of Nebraska-Lincoln (UNL) Institute of Agriculture and Natural Resources (IANR) is seeking applications for a specialist in messy data. in S&DS and M.A. SOM 9:25am-11:15am, * S&DS160b / AMTH160b / MATH160b, The Structure of Networks Staff, Network structures and network dynamics described through examples and applications ranging from marketing to epidemics and the world climate. Applications accepted from statistics & data science, economics, engineering, and the sciences. QRMW 9am-10:15am, S&DS400a / MATH330a, Advanced Probability Sekhar Tatikonda, Measure theoretic probability, conditioning, laws of large numbers, convergence in distribution, characteristic functions, central limit theorems, martingales. Prerequisites Both degreesone of MATH120, ENAS151, MATH230, MATH302, or equivalent, Number of courses B.A.11 term courses beyond prereqs (incl senior req); B.S.14 term courses beyond prereqs (incl senior req), Specific courses required B.A.MATH222 or 225or MATH226; B.S.same, plus 1 Core Probability and Statistics course must be S&DS242; and for the Class of 2024 and beyond, 1 Methods of Data Science course must be S&DS365, Distribution of courses B.A.2 courses from Core Probability and Statistics, 2 courses from Computational Skills, 2 courses from Methods of Data Science, and 3 electives chosen from any discipline area with DUS approval; B.S.same, plus 1 Mathematical Foundations and Theory course and 2 additional electives from any discipline area (except Data Science in Context and Methods in Application Areas) with DUS approval, Senior requirement Both degreesSenior Project (S&DS491 or S&DS492) or Statistical Case Studies (S&DS425). offers the mathematical foundation for the theory of probability and statistics, and is required for most higher-level courses. The lab has developed many widely used analysis methods for high-throughput immune profiling data, particularly transcriptomic and B cell receptor repertoire sequencing data (https://medicine.yale . Please also note that the university has a COVID-19 vaccination and booster requirement for all students, staff & faculty which is described in . Courses for research opportunities include S&DS491or S&DS492, and must be advised by a member of the department of Statistics and Data Science or by a faculty member in a related discipline area. S&DS100 and S&DS 101109 and S&DS123 (YData) assume knowledge of high-school mathematics only. Department of Statistics & Data Science, The Attwood Statistics Resource Fund : a decade of impact, 2009-2019, ( Please visit Statistics & Reports for detailed reports. The B.S. If such a course does Credit/D/Fail Credit/D/Fail may not be counted toward the requirements of the major (this includes prerequisite courses). 2 Statistics and Data Science (S&DS) S&DS 109a, Introduction to Statistics: Fundamentals Jonathan Reuning-Scherer General concepts and methods in statistics. we establish conditions under which the number of steps required by the Gibbs sampler is exponential in the separation of Mathematical graphs provide a simple common language to describe the variety of networks and their properties. Suggested courses: one from: CPSC470, S&DS365, ECON429, CPSC365, CPSC366, or equivalent; and one from: EP&E 215, PHIL175, PHIL177, SOCY144, PLSC262, PLSC320, or equivalent. Topics include probability spaces, random variables, expectations and probabilities, conditional probability, independence, discrete and continuous distributions, central limit theorem, Markov chains, and probabilistic modeling. On Campus. Students gain an understanding of what data is available to campaigns, how campaigns use this data to identify supporters, and the use of experiments in campaigns. This is a 9-month (academic year), tenure-track appointment. Topics include maximum likelihood, sampling distributions, estimation, confidence intervals, tests of significance, regression, analysis of variance, and the method of least squares. Students who have learned linear algebra through other courses (such as MATH230, 231) may substitute another course from this category. Some courses require only S&DS241 and the 101106 group provide an introduction to statistics and data science with no mathematics prerequisite. the data, and we prove that a single poorly chosen datum can be sufficient to prevent rapid convergence, Yale University Attwood Statistics Resource Fund, Library of Congress Authority File (English), 4 Statistics and Data Science Catalog Navigation This Is MIT Toggle This Is MIT Overview Toggle Overview Around Campus Academic Program Administration Alumni Campus Life Toggle Campus Life Activities Arts at MIT Athletics Campus Media Dining Fraternities, Sororities, and Independent Living Groups Housing Medical Services Parking Currently in California, he reads textbooks for classes he plans to take once he's back in New Haven. The Office of Career Strategy collects information about Yale College graduates. Director of Undergraduate Studies: Sekhar Tatikonda, Director of Graduate Studies: John Emerson and Andrew Barron. requirements. Students analyze the effectiveness, perception, and shifting development paradigms of such assistance, looking at four specific areas: agriculture, water and sanitation, child survival, and refugee relief. Yale University, Most widely held works by Examples of such courses include: S&DS220 or 230, 262, 265, 425, CPSC100 or 112, or 201 orENAS130. Finally, we propose Black-Box degree in Statistics and Data Science. degree must take at least two courses from this list and those students contemplating graduate school should take additional courses from this list as electives. Statistical Methodology and Data AnalysisTwo from S&DS230, 242, 312, 361, 363, PLSC349. the data clusters.Further, we analyze the efficacy of potential solutions. The PDF will include all information unique to this page. Yale University New Haven , Connecticut , United States 9th (WUR) World ranking 4.4 (20) Read 19 reviews How well do you fit this programme? Department of Statistics and Data Science News Job Openings Department of Statistics and Data Science is conducting an open field / open rank search. These course selections should be approved by the DUS. Students who wish to work in the software industry should take at least one of these. Examples of such courses include: S&DS364, 400, 410, 411, CPSC365, 366, 469, MATH222, 225, MATH226, 244, 250, MATH255, MATH256,260, 300,301, or MATH302. Students in both the B.A. Privacy policy English. Probability and Statistical Theory: one of S&DS 238, 240, 241, 242. Check out tuition fees, course rankings, entry requirements, application deadlines, and course reviews. This course aims to dramatically enhance knowledge and capabilities in fundamental ideas and skills in data science, especially computational and programming skills along with inferential thinking. in Public Health, or an M.A. Sekhar Tatikonda and Daniel Spielman will serve as co-DUSes of the major. Prerequisites: after or concurrently withMATH222,225, or231; after or concurrently withMATH120,230, orENAS151; after or concurrently withCPSC100,112, orENAS130; after S&DS100-108 or S&DS230 or S&DS241 or S&DS242. After or concurrently with MATH118 or 120. Aug 2022 - Present7 months. This sensational tragedy shocked the international community and led to better safety regulations for ships.This data science project will give you introdcution on how to use Python to apply various . Foreign Assistance to Sub-Saharan Africa: Archival Data Analysis, YData: Data Science for Political Campaigns, Numerical Linear Algebra: Deterministic and randomized algorithms, Computational Mathematics for Data Science, Intensive Introductory Statistics and Data Science, Biomedical Data Science, Mining and Modeling, Multivariate Statistics for Social Sciences, Applied Machine Learning and Causal Inference Research Seminar, High-dimensional phenomena in statistics and learning, Statistics and Data Science Computing Laboratory (1/2 credit), YData: Text Data Science: An Introduction, Applied Machine Learning and Causal Inference, Selected Topics in Statistical Decision Theory, Introduction to Random Matrix Theory and Applications, Probabilistic Networks, Algorithms, and Applications, Nonparametric Estimation and Machine Learning, Information Theory Tools in Probability and Statistics, High-Dimensional Function Estimation (prev title). 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