Logo

Introduction to Probability, Statistics, and Random Processes

Large book cover: Introduction to Probability, Statistics, and Random Processes

Introduction to Probability, Statistics, and Random Processes
by

Publisher: Kappa Research, LLC
ISBN/ASIN: 0990637204
ISBN-13: 9780990637202
Number of pages: 744

Description:
This book introduces students to probability, statistics, and stochastic processes. It can be used by both students and practitioners in engineering, various sciences, finance, and other related fields. It provides a clear and intuitive approach to these topics while maintaining mathematical accuracy.

Home page url

Download or read it online for free here:
Read online
(online html)

Similar books

Book cover: Principles of Data AnalysisPrinciples of Data Analysis
by - Prasenjit Saha
This is a short book about the principles of data analysis. The emphasis is on why things are done rather than on exactly how to do them. If you already know something about the subject, then working through this book will deepen your understanding.
(15189 views)
Book cover: Advanced Data Analysis from an Elementary Point of ViewAdvanced Data Analysis from an Elementary Point of View
by - Cambridge University Press
This is a draft textbook on data analysis methods, intended for a one-semester course for advance undergraduate students who have already taken classes in probability, mathematical statistics, and linear regression. It began as the lecture notes.
(11274 views)
Book cover: Random Matrix Models and Their ApplicationsRandom Matrix Models and Their Applications
by - Cambridge University Press
The book covers broad areas such as topologic and combinatorial aspects of random matrix theory; scaling limits, universalities and phase transitions in matrix models; universalities for random polynomials; and applications to integrable systems.
(17182 views)
Book cover: Bayesian Field TheoryBayesian Field Theory
by - arXiv.org
A particular Bayesian field theory is defined by combining a likelihood model, providing a probabilistic description of the measurement process, and a prior model, providing the information necessary to generalize from training to non-training data.
(7311 views)