機率論

Probability: A Lively Introduction

+作者:

Tijms

+年份:
2018 年1 版
+ISBN:
9781108407847
+書號:
PS0459P
+規格:
平裝/單色
+頁數:
560
+出版商:
Cambridge
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Probability has applications in many areas of modern science, not to mention in our daily life. Its importance as a mathematical discipline cannot be overrated, and it is a fascinating and surprising topic in its own right. This engaging textbook with its easy-to-follow writing style provides a comprehensive, yet concise introduction to the subject. It covers all of the standard material for undergraduate and first-year-graduate-level courses as well as many topics that are usually not found in standard text - such as Bayesian inference, Markov chain Monte Carlo simulation, and Chernoff bounds.

  • ●Stresses why probability is so relevant and how to apply it - students won't simply learn probability, they will understand it
  • Perfectly balances theory and applications - the fundamental concepts are explained with engaging real-world examples and many problem-solving tips are provided
  • Includes more than 750 problems with detailed solutions of the odd-numbered problems - students will build up confidence in their own problem-solving skills

Henk Tijms is emeritus professor at the Vrije University, Amsterdam. He is the author of several textbooks and numerous papers on applied probability and stochastic optimization. In 2008, Henk Tijms received the prestigious INFORMS Expository Writing Award for his publications and books. His activities also include the popularization of probability to high school students and the general public; he also regularly contributed to the Numberplay blog of the New York Times with probability puzzles.

1. Foundations of probability theory
2. Conditional probability
3. Discrete random variables
4. Continuous random variables
5. Jointly distributed random variables
6. Multivariate normal distribution
7. Conditioning by random variables
8. Generating functions
9. Additional topics in probability
10. Discrete-time Markov chains
11. Continuous-time Markov chains