Stochastics

This expanded edition of the classic work on empirical processes now boasts several new proved theorems not in the first.

Now available in paperback for the first time; essential reading for all students of probability theory.

Now available in paperback for the first time; essential reading for all students of probability theory.

A rigorous, unfussy introduction to modern probability theory that focuses squarely on applications in finance.

A rigorous, unfussy introduction to modern probability theory that focuses squarely on applications in finance.

This book presents introductions to the essential mathematical aspects of complexity science, suitable for advanced undergraduate/masters-level students and researchers.

Michael Harrison returns to an important topic in stochastic process theory, and illustrates its many influential applications in business and economics.

This book presents introductions to the essential mathematical aspects of complexity science, suitable for advanced undergraduate/masters-level students and researchers.

Michael Harrison returns to an important topic in stochastic process theory, and illustrates its many influential applications in business and economics.

The definitive textbook on stochastic processes, written by one of the world''s leading information theorists, covering both theory and applications.

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

The definitive textbook on stochastic processes, written by one of the world''s leading information theorists, covering both theory and applications.

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

Using everyday examples to demystify probability, this classic is now in its third edition with new chapters, exercises and examples.

Comprehensive guide to stochastic processes.

For students in pure and applied probability; lots of applications, fairly self-contained.

Provides graduate students and practitioners in physics and economics with a better understanding of stochastic processes.

Provides graduate students and practitioners in physics and economics with a better understanding of stochastic processes.

For students in pure and applied probability; lots of applications, fairly self-contained.

The first comprehensive account of controlled diffusions with a focus on ergodic or ''long run average'' control.

Now in its second edition, this book covers two major classes of mixed effects models-linear mixed models and generalized linear mixed models-and it presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields.

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.

We all know what randomness is.

This book aims to present a comprehensive, self-contained, and concise overview of extreme value theory for time series, incorporating the latest research trends alongside classical methodology.

The first unified presentation of the general asymptotic theory of compound renewal processes, with classical and functional limit theorems.

A how-to guide and scientific tutorial covering the universe of reinforcement learning and control theory for online decision making.

A study of large deviations for empirical measures and vector-valued additive functionals of general state space Markov chains.

Gathers all the main results on the spectral representation of one-dimensional Markov processes, with examples and applications.

Unsupervised domain adaptation (UDA) is a challenging problem in machine learning where the model is trained on a source domain with labeled data and tested on a target domain with unlabeled data.

This book deals with certain important problems in Classical and Quantum Information Theory Quantum Information Theory, A Selection of Matrix Inequalities Stochastic Filtering Theory Applied to Electromagnetic Fields and Strings Wigner-distributions in Quantum Mechanics Quantization of Classical Field Theories Statistical Signal Processing Quantum Field Theory, Quantum Statistics, Gravity, Stochastic Fields and Information Problems in Information Theory It will be very helpful for students of Undergraduate and Postgraduate Courses in Electronics, Communication and Signal Processing.

This book deals with certain important problems in Classical and Quantum Information Theory Quantum Information Theory, A Selection of Matrix Inequalities Stochastic Filtering Theory Applied to Electromagnetic Fields and Strings Wigner-distributions in Quantum Mechanics Quantization of Classical Field Theories Statistical Signal Processing Quantum Field Theory, Quantum Statistics, Gravity, Stochastic Fields and Information Problems in Information Theory It will be very helpful for students of Undergraduate and Postgraduate Courses in Electronics, Communication and Signal Processing.

Discrete stochastic models are tools that allow us to understand, control, and optimize engineering systems and processes.

Discrete stochastic models are tools that allow us to understand, control, and optimize engineering systems and processes.

This book provides a rigorous introduction to the theory, computation, and applications of variational inequalities (VIs), with a focus on applications in management science and finance.

With the boom of big data and machine learning and the subsequent need for parallel processing technologies, fork-join queues are more relevant now than ever before.

With the boom of big data and machine learning and the subsequent need for parallel processing technologies, fork-join queues are more relevant now than ever before.

Recognized as a "e;Recommended"e; title by Choice for their April 2021 issue.

Recognized as a "e;Recommended"e; title by Choice for their April 2021 issue.

Inverse problems of identifying parameters and initial/boundary conditions in deterministic and stochastic partial differential equations constitute a vibrant and emerging research area that has found numerous applications.

Inverse problems of identifying parameters and initial/boundary conditions in deterministic and stochastic partial differential equations constitute a vibrant and emerging research area that has found numerous applications.

This textbook discusses the use of uncertainty analysis and sensitivity analysis in environmental life cycle assessment (LCA).

Diffusion Processes, Jump Processes, and Stochastic Differential Equations provides a compact exposition of the results explaining interrelations between di?

Diffusion Processes, Jump Processes, and Stochastic Differential Equations provides a compact exposition of the results explaining interrelations between di?

Focusing on the importance of the application of statistical techniques, this book covers the design of experiments and stochastic modeling in textile engineering.

Focusing on the importance of the application of statistical techniques, this book covers the design of experiments and stochastic modeling in textile engineering.

Markov Random Flights is the first systematic presentation of the theory of Markov random flights in the Euclidean spaces of different dimensions.

Markov Random Flights is the first systematic presentation of the theory of Markov random flights in the Euclidean spaces of different dimensions.

Quantitative Methods in Transportation provides the most useful, simple, and advanced quantitative techniques for solving real-life transportation engineering problems.

Quantitative Methods in Transportation provides the most useful, simple, and advanced quantitative techniques for solving real-life transportation engineering problems.