An Introduction to Probabilistic Number Theory, Kowalski Emmanuel
Автор: James Gareth Название: An Introduction to Statistical Learning ISBN: 1461471370 ISBN-13(EAN): 9781461471370 Издательство: Springer Рейтинг: Цена: 60550.00 T Наличие на складе: Невозможна поставка. Описание: This book presents key modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and clustering.
Автор: Joseph K. Blitzstein, Jessica Hwang Название: Introduction to Probability, Second Edition ISBN: 1138369918 ISBN-13(EAN): 9781138369917 Издательство: Taylor&Francis Рейтинг: Цена: 74510.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: 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.
Автор: Koller Daphne, Friedman Nir Название: Probabilistic Graphical Models: Principles and Techniques ISBN: 0262013193 ISBN-13(EAN): 9780262013192 Издательство: MIT Press Рейтинг: Цена: 141070.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions.
Most tasks require a person or an automated system to reason -- to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.
Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.
Highly recommended, the best-selling first edition of Introduction to Scientific Programming and Simulation Using R was lauded as an excellent, easy-to-read introduction with extensive examples and exercises. This second edition continues to introduce scientific programming and stochastic modelling in a clear, practical, and thorough way. Readers learn programming by experimenting with the provided R code and data.
The book's four parts teach:
Core knowledge of R and programming concepts
How to think about mathematics from a numerical point of view, including the application of these concepts to root finding, numerical integration, and optimisation
Essentials of probability, random variables, and expectation required to understand simulation
Stochastic modelling and simulation, including random number generation and Monte Carlo integration
In a new chapter on systems of ordinary differential equations (ODEs), the authors cover the Euler, midpoint, and fourth-order Runge-Kutta (RK4) schemes for solving systems of first-order ODEs. They compare the numerical efficiency of the different schemes experimentally and show how to improve the RK4 scheme by using an adaptive step size.
Another new chapter focuses on both discrete- and continuous-time Markov chains. It describes transition and rate matrices, classification of states, limiting behaviour, Kolmogorov forward and backward equations, finite absorbing chains, and expected hitting times. It also presents methods for simulating discrete- and continuous-time chains as well as techniques for defining the state space, including lumping states and supplementary variables.
Building readers' statistical intuition, Introduction to Scientific Programming and Simulation Using R, Second Edition shows how to turn algorithms into code. It is designed for those who want to make tools, not just use them. The code and data are available for download from CRAN.
Автор: Stapor Katarzyna Название: Introduction to Probabilistic and Statistical Methods with Examples in R ISBN: 3030457982 ISBN-13(EAN): 9783030457983 Издательство: Springer Рейтинг: Цена: 79190.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The content is divided into three basic parts: the first includes elements of probability theory, the second introduces readers to the basics of descriptive and inferential statistics (estimation, hypothesis testing), and the third presents the elements of correlation and linear regression analysis.
Автор: Morris W. Hirsch Название: Differential Equations, Dynamical Systems, and an Introduction to Chaos ISBN: 0123820103 ISBN-13(EAN): 9780123820105 Издательство: Elsevier Science Рейтинг: Цена: 88690.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Suitable for students in the fields of mathematics, science, and engineering, this title provides a theoretical approach to dynamical systems and chaos. It helps them to analyze the types of differential equations that arise in their area of study.
Автор: Apostol Tom M Название: Introduction to Analytic Number Theory ISBN: 1441928057 ISBN-13(EAN): 9781441928054 Издательство: Springer Рейтинг: Цена: 49330.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: "This book is the first volume of a two-volume textbook for undergraduates and is indeed the crystallization of a course offered by the author at the California Institute of Technology to undergraduates without any previous knowledge of number theory.
Автор: P.D.T.A. Elliott Название: Probabilistic Number Theory II ISBN: 1461299942 ISBN-13(EAN): 9781461299943 Издательство: Springer Рейтинг: Цена: 81050.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: In this volume we study the value distribution of arithmetic functions, allowing unbounded renormalisations. A central problem is to decide when an additive arithmetic function fin) admits a renormalisation by real functions a(x) and {3(x) > 0 so that asx ~ 00 the frequencies vx(n;f (n) - a(x) :s;;
Автор: Andrew Granville; Ze?v Rudnick Название: Equidistribution in Number Theory, An Introduction ISBN: 1402054033 ISBN-13(EAN): 9781402054037 Издательство: Springer Рейтинг: Цена: 144410.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Written for graduate students and researchers, this work provides an introduction to the concept of equidistribution in number theory, which is of importance in many areas, including cryptography, zeros of L-functions, Heegner points, prime number theory, the theory of quadratic forms, and the arithmetic aspects of quantum chaos.
Казахстан, 010000 г. Астана, проспект Туран 43/5, НП2 (офис 2) ТОО "Логобук" Тел:+7 707 857-29-98 ,+7(7172) 65-23-70 www.logobook.kz
