Probability and computing : randomized algorithms and probabilistic analysis / Michael Mitzenmacher, Eli Upfal
Tipo de material:
TextoDetalles de publicación: New York : Cambridge University Press, ©2005Descripción: xvi, 352 páginas : ilustraciones ; 26 x 19 centímetrosTipo de contenido: - texto
- sin medio
- volumen
- 0521835402 (alk. paper)
- 978521835404 (Hardback)
- QA 274 .M574 2005
| Tipo de ítem | Biblioteca actual | Biblioteca de origen | Colección | Signatura topográfica | Copia número | Estado | Notas | Fecha de vencimiento | Código de barras | Reserva de ítems | |
|---|---|---|---|---|---|---|---|---|---|---|---|
Libros para consulta en sala
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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
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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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| QA273 L5518 Probabilidad / | QA273 L5518 Probabilidad / | QA 273 R47 A probability path / | QA 274 .M574 2005 Probability and computing : randomized algorithms and probabilistic analysis / | QA275 S856 Estadística / | QA275 S856 2002 Estadística / | QA275 S856 2002 Estadística / |
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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