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
[email protected]. This is an open access book.
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