Probability through Algebra, Bowen Kerins, Benjamin Sinwell, Al Cuoco, Glenn Stevens

Автор: Barber
Название: Bayesian Reasoning and Machine Learning
ISBN: 0521518148 ISBN-13(EAN): 9780521518147
Издательство: Cambridge Academ
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Цена: 73920.00 T
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Описание: 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.

Автор: Franz
Название: Probability on Real Lie Algebras
ISBN: 110712865X ISBN-13(EAN): 9781107128651
Издательство: Cambridge Academ
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Цена: 116160.00 T
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Описание: This monograph is a progressive introduction to non-commutativity in probability theory, summarizing and synthesizing recent results about classical and quantum stochastic processes on Lie algebras. This book will appeal to advanced undergraduate and graduate students interested in the relations between algebra, probability, and quantum theory.

Автор: Mario Lefebvre
Название: Basic Probability Theory with Applications
ISBN: 0387749942 ISBN-13(EAN): 9780387749945
Издательство: Springer
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Цена: 69870.00 T
Наличие на складе: Невозможна поставка.
Описание: Presents elementary probability theory with applications that illustrate the theory. This book reviews the basic elements of differential calculus which are used in the material to follow. It is suitable for students in pure and applied sciences such as mathematics, engineering, computer science, finance and economics.

Автор: Bradley Efron and Trevor Hastie
Название: Computer Age Statistical Inference
ISBN: 1107149894 ISBN-13(EAN): 9781107149892
Издательство: Cambridge Academ
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Цена: 60190.00 T
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Описание: The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.

Автор: Durrett, Rick
Название: Elementary probability for applications
ISBN: 0521867568 ISBN-13(EAN): 9780521867566
Издательство: Cambridge Academ
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Цена: 70740.00 T
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Описание: This is a perfect one-semester introduction to probability, for students who are familiar with basic calculus. The lively style reflects the author`s philosophy that the best way to learn probability is to see it in action, and he gives over 200 examples from genetics, sports, finance, and current events.

Автор: Joshi
Название: Introduction to Mathematical Portfolio Theory
ISBN: 1107042313 ISBN-13(EAN): 9781107042315
Издательство: Cambridge Academ
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Цена: 60190.00 T
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Описание: A concise yet comprehensive guide to the mathematics of portfolio theory from a modelling perspective, with discussion of the assumptions, limitations and implementations of the models as well as the theory underlying them. Aimed at advanced undergraduates, this book can be used for self-study or as a course text.

Автор: Boyd Stephen
Название: Introduction to Applied Linear Algebra
ISBN: 1316518965 ISBN-13(EAN): 9781316518960
Издательство: Cambridge Academ
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Цена: 45410.00 T
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Описание: A groundbreaking introductory textbook covering the linear algebra methods needed for data science and engineering applications. It combines straightforward explanations with numerous practical examples and exercises from data science, machine learning and artificial intelligence, signal and image processing, navigation, control, and finance.

Автор: Dickson, David C. M.
Название: Actuarial Mathematics for Life Contingent Risks
ISBN: 1107044073 ISBN-13(EAN): 9781107044074
Издательство: Cambridge Academ
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Цена: 83430.00 T
Наличие на складе: Поставка под заказ.
Описание: Actuarial Mathematics for Life Contingent Risks, 2nd edition, is the sole required text for the Society of Actuaries Exam MLC Fall 2015 and Spring 2016. It covers the entire syllabus for the SOA Exam MLC, including new sections for Spring 2016. It is ideal for university courses and for individuals preparing for professional actuarial examinations - especially the new, long-answer exam questions. Three leaders in actuarial science balance rigor with intuition and emphasize practical applications using computational techniques to provide a modern perspective on life contingencies and equip students for the products and risk structures of the future. The authors then develop a more contemporary outlook, introducing multiple state models, emerging cash flows and embedded options. The 210 exercises provide meaningful practice with both long-answer and multiple choice questions. Furthermore: • the book has been updated to include new material on discrete time Markov processes, on models involving joint lives, and on universal life insurance and participating traditional insurance • the Solutions Manual (ISBN 9781107620261), available for separate purchase, provides detailed solutions to the text's exercises.

Автор: Agarwal
Название: Statistical Methods for Recommender Systems
ISBN: 1107036070 ISBN-13(EAN): 9781107036079
Издательство: Cambridge Academ
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Цена: 50680.00 T
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Описание: Designing algorithms to recommend items such as news articles and movies to users is a challenging task in numerous web applications. The crux of the problem is to rank items based on users' responses to different items to optimize for multiple objectives. Major technical challenges are high dimensional prediction with sparse data and constructing high dimensional sequential designs to collect data for user modeling and system design. This comprehensive treatment of the statistical issues that arise in recommender systems includes detailed, in-depth discussions of current state-of-the-art methods such as adaptive sequential designs (multi-armed bandit methods), bilinear random-effects models (matrix factorization) and scalable model fitting using modern computing paradigms like MapReduce. The authors draw upon their vast experience working with such large-scale systems at Yahoo! and LinkedIn, and bridge the gap between theory and practice by illustrating complex concepts with examples from applications they are directly involved with.

Автор: Romik
Название: The Surprising Mathematics of Longest Increasing Subsequences
ISBN: 1107428823 ISBN-13(EAN): 9781107428829
Издательство: Cambridge Academ
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Цена: 46470.00 T
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Описание: This book presents for the first time to a graduate-level readership recent groundbreaking developments in probability and combinatorics related to the longest increasing subsequence problem. Its detailed, playful presentation provides a motivating entry to elegant mathematical ideas that are of interest to every mathematician and to many computer scientists, physicists and statisticians.

Автор: Blackburn
Название: Surveys in Combinatorics 2013
ISBN: 1107651956 ISBN-13(EAN): 9781107651951
Издательство: Cambridge Academ
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Цена: 59130.00 T
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Описание: This book consists of nine survey papers by internationally renowned mathematicians. It will be of interest to researchers in combinatorics, from graduate students who want an overview of several areas to advanced researchers who want to have an in-depth analysis of recent developments.

Автор: Lifshits Mikhail
Название: Random Processes by Example
ISBN: 9814522287 ISBN-13(EAN): 9789814522281
Издательство: World Scientific Publishing
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Цена: 74970.00 T
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Описание: This volume first introduces the mathematical tools necessary for understanding and working with a broad class of applied stochastic models. The toolbox includes Gaussian processes, independently scattered measures such as Gaussian white noise and Poisson random measures, stochastic integrals, compound Poisson, infinitely divisible and stable distributions and processes.Next, it illustrates general concepts by handling a transparent but rich example of a "teletraffic model." A minor tuning of a few parameters of the model leads to different workload regimes, including Wiener process, fractional Brownian motion and stable L vy process. The simplicity of the dependence mechanism used in the model enables us to get a clear understanding of long and short range dependence phenomena. The model also shows how light or heavy distribution tails lead to continuous Gaussian processes or to processes with jumps in the limiting regime. Finally, in this volume, readers will find discussions on the multivariate extensions that admit a variety of completely different applied interpretations.The reader will quickly become familiar with key concepts that form a language for many major probabilistic models of real world phenomena but are often neglected in more traditional courses of stochastic processes.