Real Analysis

Real Analysis
Author: N. L. Carothers
Publisher: Cambridge University Press
Total Pages: 420
Release: 2000-08-15
Genre: Mathematics
ISBN: 9780521497565

A text for a first graduate course in real analysis for students in pure and applied mathematics, statistics, education, engineering, and economics.

Measure, Integral and Probability

Measure, Integral and Probability
Author: Marek Capinski
Publisher: Springer Science & Business Media
Total Pages: 229
Release: 2013-06-29
Genre: Mathematics
ISBN: 1447136314

This very well written and accessible book emphasizes the reasons for studying measure theory, which is the foundation of much of probability. By focusing on measure, many illustrative examples and applications, including a thorough discussion of standard probability distributions and densities, are opened. The book also includes many problems and their fully worked solutions.

Probability for Statisticians

Probability for Statisticians
Author: Galen R. Shorack
Publisher: Springer Science & Business Media
Total Pages: 599
Release: 2006-05-02
Genre: Mathematics
ISBN: 0387227601

The choice of examples used in this text clearly illustrate its use for a one-year graduate course. The material to be presented in the classroom constitutes a little more than half the text, while the rest of the text provides background, offers different routes that could be pursued in the classroom, as well as additional material that is appropriate for self-study. Of particular interest is a presentation of the major central limit theorems via Steins method either prior to or alternative to a characteristic function presentation. Additionally, there is considerable emphasis placed on the quantile function as well as the distribution function, with both the bootstrap and trimming presented. The section on martingales covers censored data martingales.

Fixed Point Theorems and Applications

Fixed Point Theorems and Applications
Author: Vittorino Pata
Publisher: Springer Nature
Total Pages: 171
Release: 2019-09-22
Genre: Mathematics
ISBN: 3030196704

This book addresses fixed point theory, a fascinating and far-reaching field with applications in several areas of mathematics. The content is divided into two main parts. The first, which is more theoretical, develops the main abstract theorems on the existence and uniqueness of fixed points of maps. In turn, the second part focuses on applications, covering a large variety of significant results ranging from ordinary differential equations in Banach spaces, to partial differential equations, operator theory, functional analysis, measure theory, and game theory. A final section containing 50 problems, many of which include helpful hints, rounds out the coverage. Intended for Master’s and PhD students in Mathematics or, more generally, mathematically oriented subjects, the book is designed to be largely self-contained, although some mathematical background is needed: readers should be familiar with measure theory, Banach and Hilbert spaces, locally convex topological vector spaces and, in general, with linear functional analysis.

The Lebesgue Integral

The Lebesgue Integral
Author: Open University. M431 Course Team
Publisher:
Total Pages: 27
Release: 1992
Genre: Integrals, Generalized
ISBN: 9780749220686

Algorithms for Reinforcement Learning

Algorithms for Reinforcement Learning
Author: Csaba Grossi
Publisher: Springer Nature
Total Pages: 89
Release: 2022-05-31
Genre: Computers
ISBN: 3031015517

Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations. Table of Contents: Markov Decision Processes / Value Prediction Problems / Control / For Further Exploration

High-Dimensional Probability

High-Dimensional Probability
Author: Roman Vershynin
Publisher: Cambridge University Press
Total Pages: 299
Release: 2018-09-27
Genre: Business & Economics
ISBN: 1108415199

An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.

Introduction to the Mathematics of Medical Imaging

Introduction to the Mathematics of Medical Imaging
Author: Charles L. Epstein
Publisher: SIAM
Total Pages: 794
Release: 2008-01-01
Genre: Mathematics
ISBN: 9780898717792

At the heart of every medical imaging technology is a sophisticated mathematical model of the measurement process and an algorithm to reconstruct an image from the measured data. This book provides a firm foundation in the mathematical tools used to model the measurements and derive the reconstruction algorithms used in most of these modalities. The text uses X-ray computed tomography (X-ray CT) as a 'pedagogical machine' to illustrate important ideas and its extensive discussion of background material makes the more advanced mathematical topics accessible to people with a less formal mathematical education. This new edition contains a chapter on magnetic resonance imaging (MRI), a revised section on the relationship between the continuum and discrete Fourier transforms, an improved description of the gridding method, and new sections on both Grangreat's formula and noise analysis in MR-imaging. Mathematical concepts are illuminated with over 200 illustrations and numerous exercises.

Ordinary Differential Equations and Dynamical Systems

Ordinary Differential Equations and Dynamical Systems
Author: Gerald Teschl
Publisher: American Mathematical Society
Total Pages: 370
Release: 2024-01-12
Genre: Mathematics
ISBN: 147047641X

This book provides a self-contained introduction to ordinary differential equations and dynamical systems suitable for beginning graduate students. The first part begins with some simple examples of explicitly solvable equations and a first glance at qualitative methods. Then the fundamental results concerning the initial value problem are proved: existence, uniqueness, extensibility, dependence on initial conditions. Furthermore, linear equations are considered, including the Floquet theorem, and some perturbation results. As somewhat independent topics, the Frobenius method for linear equations in the complex domain is established and Sturm–Liouville boundary value problems, including oscillation theory, are investigated. The second part introduces the concept of a dynamical system. The Poincaré–Bendixson theorem is proved, and several examples of planar systems from classical mechanics, ecology, and electrical engineering are investigated. Moreover, attractors, Hamiltonian systems, the KAM theorem, and periodic solutions are discussed. Finally, stability is studied, including the stable manifold and the Hartman–Grobman theorem for both continuous and discrete systems. The third part introduces chaos, beginning with the basics for iterated interval maps and ending with the Smale–Birkhoff theorem and the Melnikov method for homoclinic orbits. The text contains almost three hundred exercises. Additionally, the use of mathematical software systems is incorporated throughout, showing how they can help in the study of differential equations.