Explore
Probability in Electrical Engineering and Computer Science
Jean Walrand
2021
0 Ungluers have
Faved this Work
Login to Fave
This revised textbook motivates and illustrates the techniques of applied probability by applications in electrical engineering and computer science (EECS). The author presents information processing and communication systems that use algorithms based on probabilistic models and techniques, including web searches, digital links, speech recognition, GPS, route planning, recommendation systems, classification, and estimation. He then explains how these applications work and, along the way, provides the readers with the understanding of the key concepts and methods of applied probability. Python labs enable the readers to experiment and consolidate their understanding. The book includes homework, solutions, and Jupyter notebooks. This edition includes new topics such as Boosting, Multi-armed bandits, statistical tests, social networks, queuing networks, and neural networks. For ancillaries related to this book, including examples of Python demos and also Python labs used in Berkeley, please email Mary James at mary.james@springer.com. This is an open access book.
This book is included in DOAB.
Why read this book? Have your say.
You must be logged in to comment.
Rights Information
Are you the author or publisher of this work? If so, you can claim it as yours by registering as an Unglue.it rights holder.Downloads
This work has been downloaded 170 times via unglue.it ebook links.
- 170 - pdf (CC BY) at OAPEN Library.
Keywords
- Applied probability
- Communications engineering / telecommunications
- Communications Engineering, Networks
- Computer science
- Computing & information technology
- deep neural networks
- Detection theory
- Electronics & communications engineering
- Expectation maximization
- hypothesis testing
- Linear and polynomial regression
- Machine learning
- Mathematical & Statistical Software
- Mathematical and Computational Engineering
- Mathematical and Computational Engineering Applications
- Mathematical theory of computation
- Mathematics
- Mathematics & science
- Maths for computer scientists
- Maths for engineers
- Matrix completion
- open access
- Probability & statistics
- Probability and Statistics in Computer Science
- probability theory
- Probability Theory and Stochastic Processes
- stochastic dynamic programming
- Stochastic gradient descent
- Stochastics
- Technology, engineering, agriculture
- Technology: general issues
Links
DOI: 10.1007/978-3-030-49995-2Editions