Showing results for "Gaussian Random Processes English"

This book serves as a standard reference, making this area accessible not only to researchers in probability and statistics, but also to graduate students and practitioners. The book assumes only a first-year graduate course in probability. Each chapter begins with a brief overview and concludes with a wide range of exercises at varying levels of difficulty. The authors supply detailed hints for the more challenging problems, and cover many advances made in recent years.

Introducing Discrete Random Processes, the definitive textbook for students and professionals in electrical engineering and related fields. This book is your gateway to understanding and applying the principles of random signals in discrete-time systems.Why this book?Bridging Theory and Practice: Seamlessly connects foundational concepts in probability and stochastic processes with practical, real-world engineering applications.Comprehensive Toolkit: Master essential a...

"Exploring Probability and Random Processes Using MATLAB®" offers a comprehensive guide to probability theory, stochastic processes, and their practical applications, focusing on intuitive understanding and MATLAB implementation. This book provides readers with a solid foundation in probability and stochastic processes while equipping them with tools and techniques for real-world scenarios.We begin with an introduction to probability theory, covering random variables, probability di...

Stochastic Analysis for Gaussian Random Processes and Fields: With Applications presents Hilbert space methods to study deep analytic properties connecting probabilistic notions. In particular, it studies Gaussian random fields using reproducing kernel Hilbert spaces (RKHSs).The book begins with preliminary results on covariance and associated RKHS

This book develops appreciation of the ingenuity involved in the mathematical treatment of random phenomena, and of the power of the mathematical methods employed in the solution of applied problems. It is intended to students interested in applications of probability to their disciplines.

This volume first introduces the mathematical tools necessary for understanding and working with a broad class of applied stochastic models. The toolbox includes Gaussian processes, independently scattered measures such as Gaussian white noise and Poisson random measures, stochastic integrals, compound Poisson, infinitely divisible and stable distributions and processes.Next, it illustrates general concepts by handling a transparent but rich example of a “teletraffic model”. A minor tuning...

This book offers an introduction to the field of stochastic analysis of Hermite processes. These selfsimilar stochastic processes with stationary increments live in a Wiener chaos and include the fractional Brownian motion, the only Gaussian process in this class.Using the Wiener chaos theory and multiple stochastic integrals, the book covers the main properties of Hermite processes and their multiparameter counterparts, the Hermite sheets. It delves into the probability distributi...

This book discusses the theory, method and application of non-Gaussian random vibration fatigue analysis and test. The main contents include statistical analysis method of non-Gaussian random vibration, modeling and simulation of non-Gaussian/non-stationary random vibration, response analysis under non-Gaussian base excitation, non-Gaussian random vibration fatigue life analysis, fatigue reliability evaluation of structural components under Gaussian/non-Gaussian random loadings, non-Gaussi...

Master the Mathematical Foundation Every Machine Learning Engineer NeedsAre you implementing machine learning algorithms without truly understanding the mathematical principles that power them? Do complex ML concepts feel like black boxes because you lack the mathematical foundation to see inside them? This comprehensive guide bridges the gap between mathematical theory and practical machine learning applications.Math for Machine Learning: Linear Al...

This book brings together a variety of non-Gaussian autoregressive-type models to analyze time-series data. This book collects and collates most of the available models in the field and provide their probabilistic and inferential properties. This book classifies the stationary time-series models into different groups such as linear stationary models with non-Gaussian innovations, linear stationary models with non-Gaussian marginal distributions, product autoregressive models and minificati...

I never intended to pick up a naked hitchhiker wearing nothing but a guitar.A guitar. Really.I don't collect guys like that (don't ask what kind of guys I do collect), but when you spot a blonde, tanned, sculpted man with a gorgeous smile and his thumb poking up and practically begging you to stop—you stop.And I definitely never thought I'd be staring into the bright blue eyes of Trevor Connor, the lead singer for Random Acts of Cra...

This textbook is based on 20 years of teaching a graduate-level course in random processes to a constituency extending beyond signal processing, communications, control, and networking, and including in particular circuits, RF and optics graduate students. In order to accommodate today’s circuits students’ needs to understand noise modeling, while covering classical material on Brownian motion, Poisson processes, and power spectral densities, the author has inserted discussions of thermal ...