{"product_id":"introduction-to-probability-for-computing-hardback-9781009309073","title":"Introduction to Probability for Computing (Hardback) 9781009309073","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eIntroduction to Probability for Computing\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003eA highly engaging and interactive undergraduate textbook specifically written for computer science courses.\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eMor Harchol-Balter (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781009309073, Cambridge University Press\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 28 September 2023\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e555 pages\u003cbr\u003e25 x 17.5 x 3 cm, 1.23 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e'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\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eLearn 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.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePreface\u003cbr\u003e Part I. Fundamentals and Probability on Events: 1. Before we start ... some mathematical basics\u003cbr\u003e 2. Probability on events\u003cbr\u003e Part II. Discrete Random Variables: 3. Probability and discrete random variables\u003cbr\u003e 4. Expectations\u003cbr\u003e 5. Variance, higher moments, and random sums\u003cbr\u003e 6. z-Transforms\u003cbr\u003e Part III. Continuous Random Variables: 7. Continuous random variables: single distribution\u003cbr\u003e 8. Continuous random variables: joint distributions\u003cbr\u003e 9. Normal distribution\u003cbr\u003e 10. Heavy tails: the distributions of computing\u003cbr\u003e 11. Laplace transforms\u003cbr\u003e Part IV. Computer Systems Modeling and Simulation: 12. The Poisson process\u003cbr\u003e 13. Generating random variables for simulation\u003cbr\u003e 14. Event-driven simulation\u003cbr\u003e Part V. Statistical Inference\u003cbr\u003e 15. Estimators for mean and variance\u003cbr\u003e 16. Classical statistical inference\u003cbr\u003e 17. Bayesian statistical inference\u003cbr\u003e Part VI. Tail Bounds and Applications: 18. Tail bounds\u003cbr\u003e 19. Applications of tail bounds: confidence intervals and balls-and-bins\u003cbr\u003e 20. Hashing algorithms\u003cbr\u003e Part VII. Randomized Algorithms: 21. Las Vegas randomized algorithms\u003cbr\u003e 22. Monte Carlo randomized algorithms\u003cbr\u003e 23. Primality testing\u003cbr\u003e Part VIII. Discrete-time Markov Chains\u003cbr\u003e 24. Discrete-time Markov chains: finite-state\u003cbr\u003e 25. Ergodicity for finite-state discrete-time Markov chains\u003cbr\u003e 26. Discrete-time Markov chains: infinite-state\u003cbr\u003e 27. A little bit of queueing theory\u003cbr\u003e References\u003cbr\u003e Index.\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Algorithms \u0026amp; data structures [\u003ca title=\"See our other books on Algorithms \u0026amp; data structures\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Algorithms%20\u0026amp;%20data%20structures%20%5BUMB%5D%22\"\u003eUMB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Cambridge University Press","offers":[{"title":"Brand New","offer_id":52460725403928,"sku":"9781009309073","price":46.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781009309073i.jpg?v=1785459364","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/introduction-to-probability-for-computing-hardback-9781009309073","provider":"Freshly Printed Books","version":"1.0","type":"link"}