Mor Harchol-Balter

Introduction to Probability for Computing (eBook, PDF)

• Format: PDF

Produktbeschreibung

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 "e;question and answer"e; 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.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 28. September 2023
• Englisch
• ISBN-13: 9781009309059
• Artikelnr.: 70909147

Autorenporträt

Mor Harchol-Balter is the Bruce J. Nelson Professor of Computer Science at Carnegie Mellon University. She is a Fellow of both ACM and IEEE. She has received numerous teaching awards, including the Herbert A. Simon Award for teaching excellence at CMU. She is also the author of the popular textbook Performance Analysis and Design of Computer Systems (Cambridge, 2013).

Inhaltsangabe

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.