In modern computer science, software engineering, and other fields, the need arises to make decisions under uncertainty. Presenting probability and statistical methods, simulation techniques, and modeling tools, Probability and Statistics for Computer Scientists helps students solve problems and make optimal decisions in uncertain conditions, select stochastic models, com In modern computer science, software engineering, and other fields, the need arises to make decisions under uncertainty. Presenting probability and statistical methods, simulation techniques, and modeling tools, Probability and Statistics for Computer Scientists helps students solve problems and make optimal decisions in uncertain conditions, select stochastic models, compute probabilities and forecasts, and evaluate performance of computer systems and networks. After introducing probability and distributions, this easy-to-follow textbook provides two course options. The first approach is a probability-oriented course that begins with stochastic processes, Markov chains, and queuing theory, followed by computer simulations and Monte Carlo methods. The second approach is a more standard, statistics-emphasized course that focuses on statistical inference, estimation, hypothesis testing, and regression. Assuming one or two semesters of college calculus, the book is illustrated throughout with numerous examples, exercises, figures, and tables that stress direct applications in computer science and software engineering. It also provides MATLAB(R) codes and demonstrations written in simple commands that can be directly translated into other computer languages. By the end of this course, advanced undergraduate and beginning graduate students should be able to read a word problem or a corporate report, realize the uncertainty involved in the described situation, select a suitable probability model, estimate and test its parameters based on real data, compute probabilities of interesting events and other vital characteristics, and make appropriate conclusions and forecasts.
Probability and Statistics for Computer Scientists
In modern computer science, software engineering, and other fields, the need arises to make decisions under uncertainty. Presenting probability and statistical methods, simulation techniques, and modeling tools, Probability and Statistics for Computer Scientists helps students solve problems and make optimal decisions in uncertain conditions, select stochastic models, com In modern computer science, software engineering, and other fields, the need arises to make decisions under uncertainty. Presenting probability and statistical methods, simulation techniques, and modeling tools, Probability and Statistics for Computer Scientists helps students solve problems and make optimal decisions in uncertain conditions, select stochastic models, compute probabilities and forecasts, and evaluate performance of computer systems and networks. After introducing probability and distributions, this easy-to-follow textbook provides two course options. The first approach is a probability-oriented course that begins with stochastic processes, Markov chains, and queuing theory, followed by computer simulations and Monte Carlo methods. The second approach is a more standard, statistics-emphasized course that focuses on statistical inference, estimation, hypothesis testing, and regression. Assuming one or two semesters of college calculus, the book is illustrated throughout with numerous examples, exercises, figures, and tables that stress direct applications in computer science and software engineering. It also provides MATLAB(R) codes and demonstrations written in simple commands that can be directly translated into other computer languages. By the end of this course, advanced undergraduate and beginning graduate students should be able to read a word problem or a corporate report, realize the uncertainty involved in the described situation, select a suitable probability model, estimate and test its parameters based on real data, compute probabilities of interesting events and other vital characteristics, and make appropriate conclusions and forecasts.
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Emil Petersen –
I have read chapters 1-9 thoroughly; 10-12 not so much. The core material is solid and well explained. Lots of examples and convenient definitions here and there. Also a ton of useful exercises. A minor comment is that the examples are not really CS problems if you ask me, but rather general problems using CS words. Like jobs to a printer, number of emails received and things like that. This should not denigrate the book, because it really is very, very useful. Much of the statistical magic goin I have read chapters 1-9 thoroughly; 10-12 not so much. The core material is solid and well explained. Lots of examples and convenient definitions here and there. Also a ton of useful exercises. A minor comment is that the examples are not really CS problems if you ask me, but rather general problems using CS words. Like jobs to a printer, number of emails received and things like that. This should not denigrate the book, because it really is very, very useful. Much of the statistical magic going on suddenly makes more sense. I have no regrets reading this (and it took quite a while).
mirela Darau –
- found it suitable for my Computer Science students. The main reasons for choosing it were the average mathematical level (theory is presented in a fairly simple mathematical context, which was great for my students), a good list of examples/exercises (ranging from very simple to rather complex, comprising computer science problems as well as famous probability ones, and others), and the convenient structure. However, I found some of its sections pretty dry (like Descriptive Statistics or Conti - found it suitable for my Computer Science students. The main reasons for choosing it were the average mathematical level (theory is presented in a fairly simple mathematical context, which was great for my students), a good list of examples/exercises (ranging from very simple to rather complex, comprising computer science problems as well as famous probability ones, and others), and the convenient structure. However, I found some of its sections pretty dry (like Descriptive Statistics or Continuous Distributions) and I wished for a couple of more complex problems in each chapter, as I find it good to challenge to the extreme also the abilities of good students :) [I used it together with J.L. Johnson's book - to provide some more hard-core stuff - and with A. Vicker's statistics book - to get some spicy, practical examples]
Darin –
This has become my favorite introductory statistics books. What makes this different from many other statistics books is that the author states things that most authors assume (incorrectly) will be obvious to the beginner. Standard introductory material is included as well as chapters on simulation and stochastic processes that will be helpful to some computer scientists. Many of the examples are based on computer science, which is a plus for computer science majors, but shouldn't be a problem f This has become my favorite introductory statistics books. What makes this different from many other statistics books is that the author states things that most authors assume (incorrectly) will be obvious to the beginner. Standard introductory material is included as well as chapters on simulation and stochastic processes that will be helpful to some computer scientists. Many of the examples are based on computer science, which is a plus for computer science majors, but shouldn't be a problem for non-computer science majors. There are some examples implemented in Matlab, although not much.
Tim –
As a CS major, I found this book perfectly geared for the intersection of Computer Science and Probabilities/Statistics. It covers the major application areas of the latter in the former in a relevant and efficient manner. It was used as the text book for a graduate level course that I took, and even though the professor turned the class into an undergraduate level statistics class, the book itself has been an excellent reference for my PhD work.
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