Addresses both theory and applications
Focuses on an a priori easier model (the CSMPs) and then deduces the results
for the PDMPs
Analyses what makes a result work and examines specific features only when
necessary to obtain more precise results
Presents approach to students and includes an introduction to numerical
schemes written for the uninitiated
This book is aimed at researchers, graduate students and engineers who would like to be
initiated to Piecewise Deterministic Markov Processes (PDMPs). A PDMP models a deterministic
mechanism modified by jumps that occur at random times. The fields of applications are
numerous : insurance and risk, biology, communication networks, dependability, supply
management, etc. Indeed, the PDMPs studied so far are in fact deterministic functions of
CSMPs (Completed Semi-Markov Processes), i.e. semi-Markov processes completed to become
Markov processes. This remark leads to considerably broaden the definition of PDMPs and
allows their properties to be deduced from those of CSMPs, which are easier to grasp. Stability
is studied within a very general framework. In the other chapters, the results become more
accurate as the assumptions become more precise. Generalized Chapman-Kolmogorov
equations lead to numerical schemes. The last chapter is an opening on processes for which
the deterministic flow of the PDMP is replaced with a Markov process. Marked point processes
play a key role throughout this book.
Due 2021-06-05
1st ed. 2021, XVI, 244 p. 16 illus., 4 illus. in color.
Hardcover : ISBN 978-3-030-70446-9
Product category : Monograph
Series : Probability Theory and Stochastic Modelling
Mathematics : Markov model
The first broad-ranging account of the history of ordinary and partial
differential equations and the calculus of variations to 1900
Provides numerous original and translated sources with advice on how to
study them
Emphasises the practical roots of theorems in pure analysis
Includes important overlooked topics, such as Riemann?fs paper on shock
waves and Thomson?fs contribution to the telegraphists?f equation
Includes numerous exercises that develop an approach to studying the
history of mathematics
This book presents a history of differential equations, both ordinary and partial, as well as the
calculus of variations, from the origins of the subjects to around 1900. Topics treated include
the wave equation in the hands of d?fAlembert and Euler; Fourier?fs solutions to the heat
equation and the contribution of Kovalevskaya; the work of Euler, Gauss, Kummer, Riemann,
and Poincare on the hypergeometric equation; Green?fs functions, the Dirichlet principle, and
Schwarz?fs solution of the Dirichlet problem; minimal surfaces; the telegraphists?f equation and
Thomson?fs successful design of the trans-Atlantic cable; Riemann?fs paper on shock waves; the
geometrical interpretation of mechanics; and aspects of the study of the calculus of variations
from the problems of the catenary and the brachistochrone to attempts at a rigorous theory by
Weierstrass, Kneser, and Hilbert. Three final chapters look at how the theory of partial
differential equations stood around 1900, as they were treated by Picard and Hadamard. There
are also extensive, new translations of original papers by Cauchy, Riemann, Schwarz, Darboux,
and Picard. The first book to cover the history of differential equations and the calculus of
variations in such breadth and detail, it will appeal to anyone with an interest in the field.
Beyond secondary school mathematics and physics, a course in mathematical analysis is the
only prerequisite to fully appreciate its contents.
Due 2021-06-25
1st ed. 2021, XXVI, 428 p. 43 illus., 11 illus. in color.
Softcover
ISBN 978-3-030-70574-9
Product category@: Undergraduate textbook
Series : Springer Undergraduate Mathematics Series
Mathematics : History of Mathematics
Provides a comprehensive and concrete illustration for the state-space model
Covers whole solutions through a consistent Bayesian approach: the batch
method by MCMC using Stan and sequential ones by Kalman/particle filter
using R
Presents advanced topics such as real-time structural change detection with
the horseshoe prior
This book provides a comprehensive and concrete illustration of time series analysis focusing
on the state-space model, which has recently attracted increasing attention in a broad range of
fields. The major feature of the book lies in its consistent Bayesian treatment regarding whole
combinations of batch and sequential solutions for linear Gaussian and general state-space
models: MCMC and Kalman/particle filter. The reader is given insight on flexible modeling in
modern time series analysis. The main topics of the book deal with the state-space model,
covering extensively, from introductory and exploratory methods to the latest advanced topics
such as real-time structural change detection. Additionally, a practical exercise using R/Stan
based on real data promotes understanding and enhances the reader?fs analytical capability.
Due 2021-06-05
1st ed. 2021, XVIII, 350 p. 124 illus.
Hardcover
ISBN 978-981-16-0710-3
Product category : Monograph
Statistics : Applied Statistics
Celebrates John J. Benedetto?fs lasting impact on harmonic analysis and its
applications
Covers a wide range of topics related to this field, illustrating the breadth of
influence that Benedetto has had
Serves as a valuable resource and reference on harmonic analysis with
chapters written by prominent and leading scholars in the area
John J. Benedetto has had a profound influence not only on the direction of harmonic analysis
and its applications, but also on the entire community of people involved in the field. The
chapters in this volume ? compiled on the occasion of his 80th birthday ? are written by
leading researchers in the field and pay tribute to John?fs many significant and lasting
achievements.Covering a wide range of topics in harmonic analysis and related areas, these
chapters are organized into four main parts: harmonic analysis, wavelets and frames, sampling
and signal processing, and compressed sensing and optimization.An introductory chapter also
provides a brief overview of John?fs life and mathematical career.This volume will be an
excellent reference for graduate students, researchers, and professionals in pure and applied
mathematics, engineering, and physics.
Due 2021-06-24
1st ed. 2021, XXX, 440 p.53 illus., 35 illus. in color.
Hardcover
ISBN 978-3-030-69636-8
Product category : Contributed volume
Series : Applied and Numerical Harmonic Analysis
Mathematics : Functional Analysis
Includes rigorously selected contributions
Offers a balance of theory and practice, written by active researchers
Presents a unified overview of the state of the art in extreme value theory
This book presents the state of the art in extreme value theory, with a collection of articles
related to a seminal paper on the bivariate extreme value distribution written by Professor
Masaaki Sibuya in 1960, demonstrating various developments of the original idea over the last
half-century. Written by active researchers, the unique combination of articles allows readers to
gain a sense of the excellence of the field, ranging from theory to practice, and the tradition of
theoretical developments motivated by practically important issues such as tsunamis and
financial crises. The contributions discuss a range of topics, including the parameter estimation
of the generalized beta distribution, resampling with the empirical beta copula, and regression
analysis on imbalanced binary data, as well as the semiparametric estimation of the upper
bound of extrema, the long-term analysis of extreme precipitation over Japanese river basins,
and various rules of thumb in hydrology
Due 2021-06-03
1st ed. 2021, X, 124 p. 35 illus.
Softcover
ISBN 978-981-16-0767-7
Product category : Brief
Series : JSS Research Series in Statistics
Statistics : Applied Statistics
Introduces locally mixed symmetric spaces with an emphasis on geometric
concepts and relations.
Focuses on examples, avoiding technicalities and assuming only a working
knowledge of real Lie groups.
Includes two chapters on Kuga fiber spaces and elliptic surfaces.
What do the classification of algebraic surfaces, Weyl's dimension formula and maximal orders
in central simple algebras have in common? All are related to a type of manifold called locally
mixed symmetric spaces in this book. The presentation emphasizes geometric concepts and
relations and gives each reader the "roter Faden", starting from the basics and proceeding
towards quite advanced topics which lie at the intersection of differential and algebraic
geometry, algebra and topology. Avoiding technicalities and assuming only a working
knowledge of real Lie groups, the text provides a wealth of examples of symmetric spaces. The
last two chapters deal with one particular case (Kuga fiber spaces) and a generalization
(elliptic surfaces), both of which require some knowledge of algebraic geometry. Of interest to
topologists, differential or algebraic geometers working in areas related to arithmetic groups,
the book also offers an introduction to the ideas for non-experts.
Due 2021-06-28
1st ed. 2021, XVIII, 607 p.
9 illus., 6 illus. in color.
Printed book
Hardcover
Printed book
Hardcover
ISBN 978-3-030-69803-4
Product category : Monograph
Series : Springer Monographs in Mathematics
Mathematics : Topological Groups, Lie Groups
Presents an essential statistical learning toolkit for practitioners in science,
industry, and other fields
Demonstrates application of the statistical learning methods in R
Includes new chapters on deep learning, survival analysis, and multiple
testing
Covers a range of topics, such as linear regression, classification, resampling
methods, shrinkage approaches, tree-based methods, support vector
machines, clustering, and deep learning
Features extensive color graphics for a dynamic learning experience
An Introduction to Statistical Learning provides an accessible overview of the field of statistical
learning, an essential toolset for making sense of the vast and complex data sets that have
emerged in fields ranging from biology to finance to marketing to astrophysics in the past
twenty years. This book presents some of the most important modeling and prediction
techniques, along with relevant applications. Topics include linear regression, classification,
resampling methods, shrinkage approaches, tree-based methods, support vector machines,
clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and realworld
examples are used to illustrate the methods presented. Since the goal of this textbook is
to facilitate the use of these statistical learning techniques by practitioners in science, industry,
and other fields, each chapter contains a tutorial on implementing the analyses and methods
presented in R, an extremely popular open source statistical software platform. Two of the
authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd
edition 2009), a popular reference book for statistics and machine learning researchers. An
Introduction to Statistical Learning covers many of the same topics, but at a level accessible to
a much broader audience. This book is targeted at statisticians and non-statisticians alike who
wish to use cutting-edge statistical learning techniques to analyze their data.
Due 2021-05-21
2nd ed. 2021, X, 426 p. 556 illus.
Hardcover
ISBN 978-1-0716-1417-4
Product category : Graduate/advanced undergraduate textbook
Series : Springer Texts in Statistics
Statistics : Statistical Theory and Methods
Provides a comprehensive standalone text for an upper-undergraduate course
on quantum computing
Assumes only basic knowledge in quantum mechanics, making it accessible to
students in physics and engineering
Enhances learning with plenty of end-of-chapter exercises
This book provides a self-contained undergraduate course on quantum computing based on
classroom-tested lecture notes. It reviews the fundamentals of quantum mechanics from the
double-slit experiment to entanglement, before progressing to the basics of qubits, quantum
gates, quantum circuits, quantum key distribution, and some of the famous quantum
algorithms. As well as covering quantum gates in depth, it also describes promising platforms
for their physical implementation, along with error correction, and topological quantum
computing. With quantum computing expanding rapidly in the private sector, understanding
quantum computing has never been so important for graduates entering the workplace or PhD
programs. Assuming minimal background knowledge, this book is highly accessible, with
rigorous step-by-step explanations of the principles behind quantum computation, further
reading, and end-of-chapter exercises, ensuring that undergraduate students in physics and
engineering emerge well prepared for the future.
Due 2021-06-09
1st ed. 2021, XVIII, 502 p. 248 illus., 36 illus. in color.
Hardcover
ISBN 978-3-030-69317-6
Softcover
ISBN 978-3-030-69319-0
Product category : Undergraduate textbook
Series : The Materials Research Society Series
Mathematics : Quantum Computing