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An Introduction to Probability and Stochastic Processes
Detailed coverage of probability theory, random variables and their functions, stochastic processes, linear system response to stochastic processes, Gaussian and Markov processes, and stochastic differential equations. 1973 edition.
Category: Mathematics. ISBN: 9780486490991

Elementary Theory of Numbers
Superb introduction to Euclidean algorithm and its consequences, congruences, continued fractions, powers of an integer modulo m, Gaussian integers, Diophantine equations, more. Problems, with answers. Bibliography.
Category: Mathematics. ISBN: 9780486663487

Statistical Inference for Discrete Time Stochastic Processes
This book offers an overview of statistical inference in stationary, discrete time stochastic processes. It reviews results in the last fifteen years, particularly on non-Gaussian sequences and semi-parametric and non-parametric analysis
Category: Mathematics. ISBN: 9788132207627

Upper and Lower Bounds for Stochastic Processes
In addition to its central focus on generic chaining, which allows for optimal bounds in Gaussian and Bernoulli processes, this volume on modern stochastic methods includes key applications and a variety of complete solutions to a number of classical problems.
Category: Mathematics. ISBN: 9783642540745

The Uncertainty in Physical Measurements
An introduction to the treatment of uncertainty in a phenomenological way, based on examples taken from laboratory experience and distinguishing the causes of uncertainty (there are more than just random errors and Gaussian distributions to consider).
Category: Technology. ISBN: 9780387786490

Measurement Uncertainties in Science and Technology
This book recasts classical Gaussian error calculus from scratch. The new edition thoroughly restructures and systematizes the text and offers numerous numerical examples, showing how tounderstand uncertainties, to localize the true values of measured values.
Category: Science. ISBN: 9783319048871

Gradually-varied Flow Profiles in Open Channels
This book shows how to solve GVF profiles using direct integration and Gaussian hypergeometric function. Lays groundwork for computing at one sweep the GVF profiles in a series of sustaining and adverse channels, which may have horizontal slopes between them.
Category: Technology. ISBN: 9783642352416

State-Space Models
This book explores developments in state-space models and their applications in economics and finance. Coverage includes nonlinear and non-Gaussian time series models, regime-switching and hidden Markov models, and continuous- or discrete-time state processes.
Category: Business. ISBN: 9781461477884

Simulation of Stochastic Processes with Given Accuracy and Reliability
Simulation has now become an integral part of research and development across many fields of study. Despite the large amounts of literature in the field of simulation and modeling, one recurring problem is the issue of accuracy and confidence level of constructed models. By outlining the new approaches and modern methods of simulation of stochastic processes, this book provides methods and tools in measuring accuracy and reliability in functional spaces. The authors explore analysis of the theory of Sub-Gaussian (including Gaussian one) and Square Gaussian random variables and processes and Cox processes. Methods of simulation of stochastic processes and fields with given accuracy and reliability in some Banach spaces are also considered. Provides an analysis of the theory of Sub-Gaussian (including Gaussian one) and Square Gaussian random variables and processes Contains information on the study of the issue of accuracy and confidence level of constructed models not found in other books on the topic Provides methods and tools in measuring accuracy and reliability in functional spaces
Category: Mathematics. ISBN: 9781785482175

Bayesian Estimation and Tracking
A practical approach to estimating and tracking dynamic systems in real-worl applications Much of the literature on performing estimation for non-Gaussian systems is short on practical methodology, while Gaussian methods often lack a cohesive derivation. Bayesian Estimation and Tracking addresses the gap in the field on both accounts, providing readers with a comprehensive overview of methods for estimating both linear and nonlinear dynamic systems driven by Gaussian and non-Gaussian noices. Featuring a unified approach to Bayesian estimation and tracking, the book emphasizes the derivation of all tracking algorithms within a Bayesian framework and describes effective numerical methods for evaluating density-weighted integrals, including linear and nonlinear Kalman filters for Gaussian-weighted integrals and particle filters for non-Gaussian cases. The author first emphasizes detailed derivations from first principles of eeach estimation method and goes on to use illustrative and detailed step-by-step instructions for each method that makes coding of the tracking filter simple and easy to understand. Case studies are employed to showcase applications of the discussed topics. In addition, the book supplies block diagrams for each algorithm, allowing readers to develop their own MATLAB® toolbox of estimation methods. Bayesian Estimation and Tracking is an excellent book for courses on estimation and tracking methods at the graduate level. The book also serves as a valuable reference for research scientists, mathematicians, and engineers seeking a deeper understanding of the topics.
Category: Technology. ISBN: 9780470621707
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