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Probability in Electrical Engineering and Computer Science
Probability in Electrical Engineering and Computer Science
Knygos.lt klubas Knygos.lt nariams
88,26 €
-30%
Įprastai
126,09 €
  • Išsiųsime per 12–18 d.d.
The book is designed for a junior/senior level course. Applications drive the material: PageRank, Multiplexing, Digital Link, Tracking, Speech Recognition, Route Planning and more. Topics include Markov chains, detection, coding, estimation, Viterbi algorithm, expectation maximization, clustering, compressed sensing, recommender systems, Kalman Filter, Markov decision problems, LQG, and channel capacity. Matlab examples are used to simulate models and to implement the algorithms. Appendices pro…
  • Leidėjas:
  • ISBN-10: 0615899366
  • ISBN-13: 9780615899367
  • Formatas: 15.2 x 22.9 x 2.3 cm, minkšti viršeliai
  • Kalba: Anglų

Probability in Electrical Engineering and Computer Science (el. knyga) (skaityta knyga) | knygos.lt

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The book is designed for a junior/senior level course. Applications drive the material: PageRank, Multiplexing, Digital Link, Tracking, Speech Recognition, Route Planning and more. Topics include Markov chains, detection, coding, estimation, Viterbi algorithm, expectation maximization, clustering, compressed sensing, recommender systems, Kalman Filter, Markov decision problems, LQG, and channel capacity. Matlab examples are used to simulate models and to implement the algorithms. Appendices provide the necessary background in basic probability and linear algebra. See https: //sites.google.com/site/walrandpeecs/home.

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  • Autorius: Jean Walrand
  • Leidėjas:
  • ISBN-10: 0615899366
  • ISBN-13: 9780615899367
  • Formatas: 15.2 x 22.9 x 2.3 cm, minkšti viršeliai
  • Kalba: Anglų

The book is designed for a junior/senior level course. Applications drive the material: PageRank, Multiplexing, Digital Link, Tracking, Speech Recognition, Route Planning and more. Topics include Markov chains, detection, coding, estimation, Viterbi algorithm, expectation maximization, clustering, compressed sensing, recommender systems, Kalman Filter, Markov decision problems, LQG, and channel capacity. Matlab examples are used to simulate models and to implement the algorithms. Appendices provide the necessary background in basic probability and linear algebra. See https: //sites.google.com/site/walrandpeecs/home.

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