TY  - GEN
AB  - 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. The companion website now has many examples of Python demos and also Python labs used in Berkeley.
AU  - Walrand, Jean,
CN  - QA273
DO  - 10.1007/978-3-030-49995-2
DO  - doi
ET  - [Second edition].
ID  - 1431084
KW  - Probabilities.
KW  - Electrical engineering
KW  - Computer science
KW  - Probabilités.
KW  - Génie électrique
KW  - Informatique
LK  - https://link.springer.com/10.1007/978-3-030-49995-2
N2  - 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. The companion website now has many examples of Python demos and also Python labs used in Berkeley.
SN  - 9783030499952
SN  - 3030499952
T1  - Probability in electrical engineering and computer science :an application-driven course /
TI  - Probability in electrical engineering and computer science :an application-driven course /
UR  - https://link.springer.com/10.1007/978-3-030-49995-2
ER  -