Stochastics

Modern developments of Random Matrix Theory as well as pedagogical approaches to the standard core of the discipline are surprisingly hard to find in a well-organized, readable and user-friendly fashion.

Markov processes are processes that have limited memory.

An Introduction to Stochastic Orders discusses this powerful tool that can be used in comparing probabilistic models in different areas such as reliability, survival analysis, risks, finance, and economics.

Introduction to Probability and Statistics for Engineers and Scientists, Sixth Edition, uniquely emphasizes how probability informs statistical problems, thus helping readers develop an intuitive understanding of the statistical procedures commonly used by practicing engineers and scientists.

Inequalities and Extremal Problems in Probability and Statistics: Selected Topics presents various kinds of useful inequalities that are applicable in many areas of mathematics, the sciences, and engineering.

Introduction to Probability Models, Twelfth Edition, is the latest version of Sheldon Ross's classic bestseller.

This book presents a collection of selected papers that represent the current variety of research on the teaching and learning of probability.

This book presents selected peer-reviewed contributions from the International Work-Conference on Time Series, ITISE 2017, held in Granada, Spain, September 18-20, 2017.

This book presents the proceedings of the international conference Particle Systems and Partial Differential Equations V, which was held at the University of Minho, Braga, Portugal, from the 28th to 30th November 2016.

This book discusses the mathematical simulation of biological systems, with a focus on the modeling of gene expression, gene regulatory networks and stem cell regeneration.

This book covers the classical theory of Markov chains on general state-spaces as well as many recent developments.

  This book gathers the most essential results, including recent ones, on linear-quadratic optimal control problems, which represent an important aspect of stochastic control.

This textbook addresses postgraduate students in applied mathematics, probability, and statistics, as well as computer scientists, biologists, physicists and economists, who are seeking a rigorous introduction to applied stochastic processes.

Taking continuous-time stochastic processes allowing for jumps as its starting and focal point, this book provides an accessible introduction to the stochastic calculus and control of semimartingales and explains the basic concepts of Mathematical Finance such as arbitrage theory, hedging, valuation principles, portfolio choice, and term structure modelling.

This book is devoted to parameter estimation in diffusion models involving fractional Brownian motion and related processes.

This is a development of the book entitled Multidimensional Second Order Stochastic Processes.

Stochastic analysis on Riemannian manifolds without boundary has been well established.

Weak convergence of stochastic processes is one of most important theories in probability theory.

This book belongs to the subject of control and systems theory.

This book provides an in-depth account of modern methods used to bound the supremum of stochastic processes.

Geometry and Statistics, Volume 46 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters written by an international board of authors.

This book presents a selection of peer-reviewed contributions to the fifth Bayesian Young Statisticians Meeting, BaYSM 2021, held virtually due to the COVID-19 pandemic on 1-3 September 2021.

This book presents techniques for determining uncertainties in numerical solutions with applications in the fields of business administration, civil engineering, and economics, using Excel as a computational tool.

This book introduces the reader to Six Sigma, a problem-solving technique for reducing defects and variation in processes.

This book develops the theory of continuous and discrete stochastic processes within the context of cell biology.

This book is a detailed introduction to selective maintenance and updates readers on recent advances in this field, emphasizing mathematical formulation and optimization techniques.

This book is a rigorous but practical presentation of the techniques of uncertainty quantification, with applications in R and Python.

This volume presents a selection of texts that reflects the current research streams in probability, with an interest toward topics such as filtrations, Markov processes and Markov chains as well as  large deviations, Stochastic Partial Differential equations, rough paths theory, quantum probabilities and percolation on graphs.

This volume is a collection of solicited and refereed articles from distinguished researchers across the field of stochastic analysis and its application to finance.

The book provides an introduction to advanced topics in stochastic processes and related stochastic analysis, and combines them with a sound presentation of the fundamentals of financial mathematics.

The study of measure-valued processes in random environments has seen some intensive research activities in recent years whereby interesting nonlinear stochastic partial differential equations (SPDEs) were derived.

This book provides a contemporary treatment of the problems related to anomalous diffusion and anomalous relaxation.

This book shows the breadth and depth of stochastic programming applications.

This book focuses on analysing the applications of the Shannon Measure of Information (SMI).

This book helps readers easily learn basic model checking by presenting examples, exercises and case studies.

This volume is an original collection of articles by 44 leading mathematicians on the theme of the future of the discipline.

This book provides robust analysis and synthesis tools for Markovian jump systems in the finite-time domain with specified performances.

This textbook integrates scientific programming with the use of R and uses it both as a tool for applied problems and to aid in learning calculus ideas.

A Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.

This book describes extensions of Sudakov's classical result on the concentration of measure phenomenon for weighted sums of dependent random variables.

The book offers an introduction to the technical foundation of decentralized insurance models, for advanced undergraduate students, graduate students and practitioners.

How does chance enter our world?

This is the first comprehensive book on Trotter-Kato approximations of stochastic differential equations (SDEs) in infinite dimensions and applications.

This text presents the basic theory of random walks on infinite, finitely generated groups, along with certain background material in measure-theoretic probability.

This text presents the basic theory of random walks on infinite, finitely generated groups, along with certain background material in measure-theoretic probability.

This book describes how reliability can be embedded into the product development using a design methodology that uses parametric accelerated lifecycle testing (ALT) .

This textbook integrates scientific programming with the use of R and uses it both as a tool for applied problems and to aid in learning calculus ideas.

This monograph has arisen out of a number of attempts spanning almost five decades to understand how one might examine the evolution of densities in systems whose dynamics are described by differential delay equations.