Introduction to Probability for Computing
£54.99
- Author: Mor Harchol-Balter, Carnegie Mellon University, Pennsylvania
- Date Published: September 2023
- availability: In stock
- format: Hardback
- isbn: 9781009309073
£
54.99
Hardback
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Learn about probability as it is used in computer science with this rigorous, yet highly accessible, undergraduate textbook. Fundamental probability concepts are explained in depth, prerequisite mathematics is summarized, and a wide range of computer science applications is described. Throughout, the material is presented in a “question and answer” style designed to encourage student engagement and understanding. Replete with almost 400 exercises, real-world computer science examples, and covering a wide range of topics from simulation with computer science workloads, to statistical inference, to randomized algorithms, to Markov models and queues, this interactive text is an invaluable learning tool whether your course covers probability with statistics, with stochastic processes, with randomized algorithms, or with simulation. The teaching package includes solutions, lecture slides, and lecture notes for students.
Read more- Designed to teach students about probability as it is used in computer science curricula today
- Includes many examples and applications specific to computer science, including hash table design, capacity provisioning in data centers, web page ranking, disk modeling, virus propagation, deducing signals in noisy environments, error correction, caching, and primality testing
- Fully rigorous, yet accessible for students, with an interactive question-and-answer style focusing on intuition-building
- Includes full-color illustrations, numerous examples, and nearly 400 exercises
Reviews & endorsements
'Based on 20 years of teaching Computer Science and Operations Research at Carnegie Mellon University, Professor Harchol-Balter provides a unique presentation of probability and statistics that is both highly engaging and also strongly motivated by realworld computing applications that students will encounter in industry. This book is approachable and fun for undergraduate students, while also covering advanced concepts relevant to graduate students.' Eytan Modiano, Massachusetts Institute of Technology
See more reviews'This book provides a fantastic introduction to probability for computer scientists and computing professionals, addressing concepts and techniques crucial to the design and analysis of randomized algorithms, to performing well-designed simulations, to statistical inference and machine learning, and more. Also contains many great exercises and examples. Highly recommend!' Avrim Blum, Toyota Technological Institute at Chicago
'Mor Harchol-Balter's new book does a beautiful job of introducing students to probability! The book is full of great computer science-relevant examples, wonderful intuition, simple and clear explanations, and mathematical rigor. I love the question-answer style she uses, and could see using this book for students ranging from undergraduate students with zero prior exposure to probability all the way to graduate students (or researchers of any kind) who need to brush up and significantly deepen (and/or broaden) their knowledge of probability.' Anna Karlin, University of Washington
'Probability is at the heart of modeling, design, and analysis of computer systems and networks. This book by a pioneer in the area is a beautiful introduction to the topic for undergraduate students. The material in the book introduces theoretical topics rigorously, but also motivates each topicwith practical applications. This textbook is an excellent resource for budding computer scientists who are interested in probability.' R. Srikant, University of Illinois at Urbana-Champaign
'I know probability theory, and have taught it to undergrads and grads at MIT, UC Berkeley, and Carnegie Mellon University. Yet this book has taught me some wonderfully interesting important material that I did not know. Mor is a great thinker, lecturer, and writer. I would love to have learned from this book as a student - and to have taught from it as an instructor!' Manuel Blum, University of California, Berkeley, and Carnegie Mellon University
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×Product details
- Date Published: September 2023
- format: Hardback
- isbn: 9781009309073
- length: 555 pages
- dimensions: 250 x 175 x 30 mm
- weight: 1.23kg
- availability: In stock
Table of Contents
Preface
Part I. Fundamentals and Probability on Events:
1. Before we start ... some mathematical basics
2. Probability on events
Part II. Discrete Random Variables:
3. Probability and discrete random variables
4. Expectations
5. Variance, higher moments, and random sums
6. z-Transforms
Part III. Continuous Random Variables:
7. Continuous random variables: single distribution
8. Continuous random variables: joint distributions
9. Normal distribution
10. Heavy tails: the distributions of computing
11. Laplace transforms
Part IV. Computer Systems Modeling and Simulation:
12. The Poisson process
13. Generating random variables for simulation
14. Event-driven simulation
Part V. Statistical Inference
15. Estimators for mean and variance
16. Classical statistical inference
17. Bayesian statistical inference
Part VI. Tail Bounds and Applications:
18. Tail bounds
19. Applications of tail bounds: confidence intervals and balls-and-bins
20. Hashing algorithms
Part VII. Randomized Algorithms:
21. Las Vegas randomized algorithms
22. Monte Carlo randomized algorithms
23. Primality testing
Part VIII. Discrete-time Markov Chains
24. Discrete-time Markov chains: finite-state
25. Ergodicity for finite-state discrete-time Markov chains
26. Discrete-time Markov chains: infinite-state
27. A little bit of queueing theory
References
Index.
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