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Probability and computing : randomized algorithms and probabilistic analysis / Michael Mitzenmacher, Eli Upfal

Por: Colaborador(es): Tipo de material: TextoTextoDetalles de publicación: New York : Cambridge University Press, ©2005Descripción: xvi, 352 páginas : ilustraciones ; 26 x 19 centímetrosTipo de contenido:
  • texto
Tipo de medio:
  • sin medio
Tipo de soporte:
  • volumen
ISBN:
  • 0521835402 (alk. paper)
  • 978521835404 (Hardback)
Tema(s): Clasificación LoC:
  • QA 274 .M574 2005
Recursos en línea:
Contenidos:
1. Events and probability -- 2. Discrete random variables and expectation -- 3. Moments and deviations 4. Chernoff bounds -- 5. Balls, bins and random graphs -- 6. The probabilistic method -- 7. Markov chains and random walks -- 8. Continuous distributions and the Poisson process -- 9. Entropy, randomness, and information -- 10. The Monte Carlo method -- 11. Coupling of Markov chains 12. Martingales -- 13. Pairwise independence and universal hash functions -- 14. Balanced allocations
Resumen: " Randomization and probabilistic techniques play an important role in modern computer science, with applications ranging from combinatorial optimization and machine learning to communication networks and secure protocols. This textbook is designed to accompany a one- or two-semester course for advanced undergraduates or beginning graduate students in computer science and applied mathematics. It gives an excellent introduction to the probabilistic techniques and paradigms used in the development of probabilistic algorithms and analyses. It assumes only an elementary background in discrete mathematics and gives a rigorous yet accessible treatment of the material, with numerous examples and applications. The first half of the book covers core material, including random sampling, expectations, Markov's inequality, Chevyshev's inequality, Chernoff bounds, balls and bins models, the probabilistic method, and Markov chains. In the second half, the authors delve into more advanced topics such as continuous probability, applications of limited independence, entropy, Markov chain Monte Carlo methods, coupling, martingales, and balanced allocations. With its comprehensive selection of topics, along with many examples and exercises, this book is an indispensable teaching tool. " -- P. [4]
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Libros para consulta en sala Libros para consulta en sala Biblioteca Antonio Enriquez Savignac Biblioteca Antonio Enriquez Savignac COLECCIÓN RESERVA QA 274 .M574 2005 (Navegar estantería(Abre debajo)) 1 No para préstamo Ing. Telematica 036080
Libros Libros Biblioteca Antonio Enriquez Savignac Biblioteca Antonio Enriquez Savignac Colección General QA 274 .M574 2005 (Navegar estantería(Abre debajo)) 2 Disponible Ing. Telematica 036081
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Incluye índice

1. Events and probability -- 2. Discrete random variables and expectation -- 3. Moments and deviations 4. Chernoff bounds -- 5. Balls, bins and random graphs -- 6. The probabilistic method -- 7. Markov chains and random walks -- 8. Continuous distributions and the Poisson process -- 9. Entropy, randomness, and information -- 10. The Monte Carlo method -- 11. Coupling of Markov chains 12. Martingales -- 13. Pairwise independence and universal hash functions -- 14. Balanced allocations

" Randomization and probabilistic techniques play an important role in modern computer science, with applications ranging from combinatorial optimization and machine learning to communication networks and secure protocols. This textbook is designed to accompany a one- or two-semester course for advanced undergraduates or beginning graduate students in computer science and applied mathematics. It gives an excellent introduction to the probabilistic techniques and paradigms used in the development of probabilistic algorithms and analyses. It assumes only an elementary background in discrete mathematics and gives a rigorous yet accessible treatment of the material, with numerous examples and applications. The first half of the book covers core material, including random sampling, expectations, Markov's inequality, Chevyshev's inequality, Chernoff bounds, balls and bins models, the probabilistic method, and Markov chains. In the second half, the authors delve into more advanced topics such as continuous probability, applications of limited independence, entropy, Markov chain Monte Carlo methods, coupling, martingales, and balanced allocations. With its comprehensive selection of topics, along with many examples and exercises, this book is an indispensable teaching tool. " -- P. [4]

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