Python for Probability, Statistics, and Machine Learning, Unpingco Jose
Àâòîð: Gauch, Jr Íàçâàíèå: Scientific Method in Brief ISBN: 1107666724 ISBN-13(EAN): 9781107666726 Èçäàòåëüñòâî: Cambridge Academ Ðåéòèíã: Öåíà: 43290.00 T Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç. Îïèñàíèå: Specifically designed to enhance perspective and encourage productivity, this is a guide to the key principles of scientific method including deductive and inductive logic, probability, parsimony and hypothesis testing. The examples and case studies span the physical, biological and social sciences and also highlight science`s interrelationship with the humanities.
Àâòîð: Morgan Íàçâàíèå: Counterfactuals and Causal Inference ISBN: 1107694167 ISBN-13(EAN): 9781107694163 Èçäàòåëüñòâî: Cambridge Academ Ðåéòèíã: Öåíà: 38010.00 T Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç. Îïèñàíèå: Cause-and-effect questions are the motivation for most research in the social, demographic, and health sciences. The counterfactual approach to causal analysis represents a unified framework for the prosecution of these questions. This second edition aims to convince more social scientists to take this approach when analyzing these core empirical questions.
Àâòîð: Devore Jay L. Íàçâàíèå: Probability and Statistics for Engineering and the Sciences ISBN: 1337094269 ISBN-13(EAN): 9781337094269 Èçäàòåëüñòâî: Cengage Learning Ðåéòèíã: Öåíà: 71800.00 T Íàëè÷èå íà ñêëàäå: Íåò â íàëè÷èè. Îïèñàíèå: Put statistical theories into practice with PROBABILITY AND STATISTICS FOR ENGINEERING AND THE SCIENCES, 9E, INTERNATIONAL METRIC EDITION. Always a market favorite, this calculus-based book offers a comprehensive introduction to probability and statistics while demonstrating how to apply concepts, models, and methodologies in today's engineering and scientific workplaces. Jay Devore, an award-winning professor and internationally recognized author and statistician, stresses lively examples and engineering activities to drive home the numbers without exhaustive mathematical development and derivations.
Many examples, practice problems, sample tests, and simulations based on real data and issues help you build a more intuitive connection to the material. A proven and accurate book, PROBABILITY AND STATISTICS FOR ENGINEERING AND THE SCIENCES, 9E, INTERNATIONAL METRIC EDITION also includes graphics and screen shots from SAS (R), MINITAB (R), and Java (TM) Applets to give you a solid perspective of statistics in action.
Àâòîð: Barber Íàçâàíèå: Bayesian Reasoning and Machine Learning ISBN: 0521518148 ISBN-13(EAN): 9780521518147 Èçäàòåëüñòâî: Cambridge Academ Ðåéòèíã: Öåíà: 73920.00 T Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç. Îïèñàíèå: This practical introduction for final-year undergraduate and graduate students is ideally suited to computer scientists without a background in calculus and linear algebra. Numerous examples and exercises are provided. Additional resources available online and in the comprehensive software package include computer code, demos and teaching materials for instructors.
Àâòîð: Gelman Íàçâàíèå: Bayesian Data Analysis, Third Edition ISBN: 1439840954 ISBN-13(EAN): 9781439840955 Èçäàòåëüñòâî: Taylor&Francis Ðåéòèíã: Öåíà: 73920.00 T Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç. Îïèñàíèå: Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
Àâòîð: Thompson John Íàçâàíèå: Bayesian Analysis with Stata ISBN: 1597181412 ISBN-13(EAN): 9781597181419 Èçäàòåëüñòâî: Taylor&Francis Ðåéòèíã: Öåíà: 57150.00 T Íàëè÷èå íà ñêëàäå: Íåâîçìîæíà ïîñòàâêà. Îïèñàíèå:
Bayesian Analysis with Stata is written for anyone interested in applying Bayesian methods to real data easily. The book shows how modern analyses based on Markov chain Monte Carlo (MCMC) methods are implemented in Stata both directly and by passing Stata datasets to OpenBUGS or WinBUGS for computation, allowing Stata's data management and graphing capability to be used with OpenBUGS/WinBUGS speed and reliability.
The book emphasizes practical data analysis from the Bayesian perspective, and hence covers the selection of realistic priors, computational efficiency and speed, the assessment of convergence, the evaluation of models, and the presentation of the results. Every topic is illustrated in detail using real-life examples, mostly drawn from medical research.
The book takes great care in introducing concepts and coding tools incrementally so that there are no steep patches or discontinuities in the learning curve. The book's content helps the user see exactly what computations are done for simple standard models and shows the user how those computations are implemented. Understanding these concepts is important for users because Bayesian analysis lends itself to custom or very complex models, and users must be able to code these themselves.
Àâòîð: Ma Íàçâàíèå: Manifold Learning Theory and Applications ISBN: 1439871094 ISBN-13(EAN): 9781439871096 Èçäàòåëüñòâî: Taylor&Francis Ðåéòèíã: Öåíà: 148010.00 T Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç. Îïèñàíèå:
Trained to extract actionable information from large volumes of high-dimensional data, engineers and scientists often have trouble isolating meaningful low-dimensional structures hidden in their high-dimensional observations. Manifold learning, a groundbreaking technique designed to tackle these issues of dimensionality reduction, finds widespread application in machine learning, neural networks, pattern recognition, image processing, and computer vision.
Filling a void in the literature, Manifold Learning Theory and Applications incorporates state-of-the-art techniques in manifold learning with a solid theoretical and practical treatment of the subject. Comprehensive in its coverage, this pioneering work explores this novel modality from algorithm creation to successful implementation--offering examples of applications in medical, biometrics, multimedia, and computer vision. Emphasizing implementation, it highlights the various permutations of manifold learning in industry including manifold optimization, large scale manifold learning, semidefinite programming for embedding, manifold models for signal acquisition, compression and processing, and multi scale manifold.
Beginning with an introduction to manifold learning theories and applications, the book includes discussions on the relevance to nonlinear dimensionality reduction, clustering, graph-based subspace learning, spectral learning and embedding, extensions, and multi-manifold modeling. It synergizes cross-domain knowledge for interdisciplinary instructions, offers a rich set of specialized topics contributed by expert professionals and researchers from a variety of fields. Finally, the book discusses specific algorithms and methodologies using case studies to apply manifold learning for real-world problems.
Àâòîð: Kai Lai Chung Íàçâàíèå: A Course in Probability Theory, Revised Edition, ISBN: 0121741516 ISBN-13(EAN): 9780121741518 Èçäàòåëüñòâî: Elsevier Science Ðåéòèíã: Öåíà: 83080.00 T Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç. Îïèñàíèå: This book is designed for undergraduate programs and students and can also be used as a first-year graduate text in probability. It offers a broad perspective, building on the synopsis of measure and integration offered in Chapter two.
Àâòîð: Joseph K. Blitzstein, Jessica Hwang Íàçâàíèå: Introduction to Probability, Second Edition ISBN: 1138369918 ISBN-13(EAN): 9781138369917 Èçäàòåëüñòâî: Taylor&Francis Ðåéòèíã: Öåíà: 74510.00 T Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç. Îïèñàíèå: Assumes one-semester of calculus. "Stories" make distributions (Normal, Binomial, Poisson that are widely-used in statistics) easier to remember, understand. Many books write down formulas without explaining clearly why these particular distributions are important or how they are all connected.
A brand new, fully updated edition of a popular classic on matrix differential calculus with applications in statistics and econometrics
This exhaustive, self-contained book on matrix theory and matrix differential calculus provides a treatment of matrix calculus based on differentials and shows how easy it is to use this theory once you have mastered the technique. Jan Magnus, who, along with the late Heinz Neudecker, pioneered the theory, develops it further in this new edition and provides many examples along the way to support it.
Matrix calculus has become an essential tool for quantitative methods in a large number of applications, ranging from social and behavioral sciences to econometrics. It is still relevant and used today in a wide range of subjects such as the biosciences and psychology. Matrix Differential Calculus with Applications in Statistics and Econometrics, Third Edition contains all of the essentials of multivariable calculus with an emphasis on the use of differentials. It starts by presenting a concise, yet thorough overview of matrix algebra, then goes on to develop the theory of differentials. The rest of the text combines the theory and application of matrix differential calculus, providing the practitioner and researcher with both a quick review and a detailed reference.
Fulfills the need for an updated and unified treatment of matrix differential calculus
Contains many new examples and exercises based on questions asked of the author over the years
Covers new developments in field and features new applications
Written by a leading expert and pioneer of the theory
Part of the Wiley Series in Probability and Statistics
Matrix Differential Calculus With Applications in Statistics and Econometrics Third Edition is an ideal text for graduate students and academics studying the subject, as well as for postgraduates and specialists working in biosciences and psychology.
Àâòîð: Michael C. Whitlock, Dolph Schluter Íàçâàíèå: The Analysis of Biological Data ISBN: 1319325343 ISBN-13(EAN): 9781319325343 Èçäàòåëüñòâî: Macmillan Learning Ðåéòèíã: Öåíà: 92390.00 T Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç. Îïèñàíèå: The evolution of a classicThe new 12th edition of Introduction to Genetic Analysis takes this cornerstone textbook to the next level.
Àâòîð: Unpingco José Íàçâàíèå: Python for Probability, Statistics, and Machine Learning ISBN: 3030185478 ISBN-13(EAN): 9783030185473 Èçäàòåëüñòâî: Springer Ðåéòèíã: Öåíà: 51230.00 T Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç. Îïèñàíèå: This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas.
