Atjaunināt sīkdatņu piekrišanu

E-grāmata: Probability and Statistics for Computer Scientists

3.84/5 (44 ratings by Goodreads)
(American University, Washington, DC)
  • Formāts: 486 pages
  • Izdošanas datums: 25-Jun-2019
  • Izdevniecība: CRC Press
  • Valoda: eng
  • ISBN-13: 9781351697408
Citas grāmatas par šo tēmu:
  • Formāts - PDF+DRM
  • Cena: 131,49 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
Probability and Statistics for Computer ScientistsPalielināt
  • Formāts: 486 pages
  • Izdošanas datums: 25-Jun-2019
  • Izdevniecība: CRC Press
  • Valoda: eng
  • ISBN-13: 9781351697408
Citas grāmatas par šo tēmu:

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

Praise for the Second Edition:

"The author has done his homework on the statistical tools needed for the particular challenges computer scientists encounter... [ He] has taken great care to select examples that are interesting and practical for computer scientists. ... The content is illustrated with numerous figures, and concludes with appendices and an index. The book is erudite and could work well as a required text for an advanced undergraduate or graduate course." ---Computing Reviews

Probability and Statistics for Computer Scientists, Third Edition helps students understand fundamental concepts of Probability and Statistics, general methods of stochastic modeling, simulation, queuing, and statistical data analysis; make optimal decisions under uncertainty; model and evaluate computer systems; and prepare for advanced probability-based courses. Written in a lively style with simple language and now including R as well as MATLAB, this classroom-tested book can be used for one- or two-semester courses.

Features:











Axiomatic introduction of probability





Expanded coverage of statistical inference and data analysis, including estimation and testing, Bayesian approach, multivariate regression, chi-square tests for independence and goodness of fit, nonparametric statistics, and bootstrap





Numerous motivating examples and exercises including computer projects





Fully annotated R codes in parallel to MATLAB





Applications in computer science, software engineering, telecommunications, and related areas

In-Depth yet Accessible Treatment of Computer Science-Related Topics Starting with the fundamentals of probability, the text takes students through topics heavily featured in modern computer science, computer engineering, software engineering, and associated fields, such as computer simulations, Monte Carlo methods, stochastic processes, Markov chains, queuing theory, statistical inference, and regression. It also meets the requirements of the Accreditation Board for Engineering and Technology (ABET).

About the Author







Michael Baron

is David Carroll Professor of Mathematics and Statistics at American University in Washington D. C. He conducts research in sequential analysis and optimal stopping, change-point detection, Bayesian inference, and applications of statistics in epidemiology, clinical trials, semiconductor manufacturing, and other fields. M. Baron is a Fellow of the American Statistical Association and a recipient of the Abraham Wald Prize for the best paper in Sequential Analysis and the Regents Outstanding Teaching Award. M. Baron holds a Ph.D. in statistics from the University of Maryland. In his turn, he supervised twelve doctoral students, mostly employed on academic and research positions.
Preface xv
1 Introduction and Overview 1(6)
1.1 Making decisions under uncertainty
1(2)
1.2 Overview of this book
3(2)
Summary and conclusions
5(1)
Exercises
5(2)
I Probability and Random Variables 7(126)
2 Probability
9(30)
2.1 Events and their probabilities
9(4)
2.1.1 Outcomes, events, and the sample space
10(1)
2.1.2 Set operations
11(2)
2.2 Rules of Probability
13(7)
2.2.1 Axioms of Probability
14(1)
2.2.2 Computing probabilities of events
15(3)
2.2.3 Applications in reliability
18(2)
2.3 Combinatorics
20(7)
2.3.1 Equally likely outcomes
20(2)
2.3.2 Permutations and combinations
22(5)
2.4 Conditional probability and independence
27(5)
Summary and conclusions
32(1)
Exercises
33(6)
3 Discrete Random Variables and Their Distributions
39(36)
3.1 Distribution of a random variable
40(4)
3.1.1 Main concepts
40(4)
3.1.2 Types of random variables
44(1)
3.2 Distribution of a random vector
44(3)
3.2.1 Joint distribution and marginal distributions
45(1)
3.2.2 Independence of random variables
46(1)
3.3 Expectation and variance
47(10)
3.3.1 Expectation
47(2)
3.3.2 Expectation of a function
49(1)
3.3.3 Properties
49(1)
3.3.4 Variance and standard deviation
50(1)
3.3.5 Covariance and correlation
51(1)
3.3.6 Properties
52(2)
3.3.7 Chebyshev's inequality
54(1)
3.3.8 Application to finance
55(2)
3.4 Families of discrete distributions
57(11)
3.4.1 Bernoulli distribution
58(1)
3.4.2 Binomial distribution
59(2)
3.4.3 Geometric distribution
61(2)
3.4.4 Negative Binomial distribution
63(2)
3.4.5 Poisson distribution
65(1)
3.4.6 Poisson approximation of Binomial distribution
66(2)
Summary and conclusions
68(1)
Exercises
68(7)
4 Continuous Distributions
75(28)
4.1 Probability density
75(5)
4.2 Families of continuous distributions
80(12)
4.2.1 Uniform distribution
80(2)
4.2.2 Exponential distribution
82(2)
4.2.3 Gamma distribution
84(5)
4.2.4 Normal distribution
89(3)
4.3 Central Limit Theorem
92(4)
Summary and conclusions
96(1)
Exercises
96(7)
5 Computer Simulations and Monte Carlo Methods
103(30)
5.1 Introduction
103(2)
5.1.1 Applications and examples
104(1)
5.2 Simulation of random variables
105(11)
5.2.1 Random number generators
106(1)
5.2.2 Discrete methods
107(3)
5.2.3 Inverse transform method
110(2)
5.2.4 Rejection method
112(2)
5.2.5 Generation of random vectors
114(1)
5.2.6 Special methods
115(1)
5.3 Solving problems by Monte Carlo methods
116(11)
5.3.1 Estimating probabilities
116(4)
5.3.2 Estimating means and standard deviations
120(1)
5.3.3 Forecasting
121(2)
5.3.4 Estimating lengths, areas, and volumes
123(2)
5.3.5 Monte Carlo integration
125(2)
Summary and conclusions
127(1)
Exercises
128(5)
II Stochastic Processes 133(78)
6 Stochastic Processes
135(36)
6.1 Definitions and classifications
136(1)
6.2 Markov processes and Markov chains
137(15)
6.2.1 Markov chains
138(4)
6.2.2 Matrix approach
142(4)
6.2.3 Steady-state distribution
146(6)
6.3 Counting processes
152(9)
6.3.1 Binomial process
152(4)
6.3.2 Poisson process
156(5)
6.4 Simulation of stochastic processes
161(3)
Summary and conclusions
164(1)
Exercises
165(6)
7 Queuing Systems
171(40)
7.1 Main components of a queuing system
172(2)
7.2 The Little's Law
174(3)
7.3 Bernoulli single-server queuing process
177(5)
7.3.1 Systems with limited capacity
181(1)
7.4 M/M/1 system
182(7)
7.4.1 Evaluating the system's performance
185(4)
7.5 Multiserver queuing systems
189(9)
7.5.1 Bernoulli k-server queuing process
190(3)
7.5.2 M/M/k systems
193(3)
7.5.3 Unlimited number of servers and M/M/infinity
196(2)
7.6 Simulation of queuing systems
198(5)
Summary and conclusions
203(1)
Exercises
203(8)
III Statistics 211(206)
8 Introduction to Statistics
213(30)
8.1 Population and sample, parameters and statistics
214(3)
8.2 Descriptive statistics
217(12)
8.2.1 Mean
217(2)
8.2.2 Median
219(4)
8.2.3 Quantiles, percentiles, and quartiles
223(2)
8.2.4 Variance and standard deviation
225(2)
8.2.5 Standard errors of estimates
227(1)
8.2.6 Interquartile range
228(1)
8.3 Graphical statistics
229(11)
8.3.1 Histogram
230(3)
8.3.2 Stem-and-leaf plot
233(2)
8.3.3 Boxplot
235(2)
8.3.4 Scatter plots and time plots
237(3)
Summary and conclusions
240(1)
Exercises
240(3)
9 Statistical Inference I
243(72)
9.1 Parameter estimation
244(10)
9.1.1 Method of moments
245(3)
9.1.2 Method of maximum likelihood
248(4)
9.1.3 Estimation of standard errors
252(2)
9.2 Confidence intervals
254(8)
9.2.1 Construction of confidence intervals: a general method
255(2)
9.2.2 Confidence interval for the population mean
257(1)
9.2.3 Confidence interval for the difference between two means
258(2)
9.2.4 Selection of a sample size
260(1)
9.2.5 Estimating means with a given precision
261(1)
9.3 Unknown standard deviation
262(9)
9.3.1 Large samples
262(1)
9.3.2 Confidence intervals for proportions
263(2)
9.3.3 Estimating proportions with a given precision
265(1)
9.3.4 Small samples: Student's t distribution
266(2)
9.3.5 Comparison of two populations with unknown variances
268(3)
9.4 Hypothesis testing
271(22)
9.4.1 Hypothesis and alternative
272(1)
9.4.2 Type I and Type II errors: level of significance
273(1)
9.4.3 Level α tests: general approach
274(2)
9.4.4 Rejection regions and power
276(1)
9.4.5 Standard Normal null distribution (Z-test)
277(2)
9.4.6 Z-tests for means and proportions
279(2)
9.4.7 Pooled sample proportion
281(1)
9.4.8 Unknown σ: T-tests
282(2)
9.4.9 Duality: two-sided tests and two-sided confidence intervals
284(3)
9.4.10 P-value
287(6)
9.5 Inference about variances
293(14)
9.5.1 Variance estimator and Chi-square distribution
293(2)
9.5.2 Confidence interval for the population variance
295(1)
9.5.3 Testing variance
296(3)
9.5.4 Comparison of two variances. F-distribution.
299(2)
9.5.5 Confidence interval for the ratio of population variances
301(2)
9.5.6 F-tests comparing two variances
303(4)
Summary and conclusions
307(1)
Exercises
308(7)
10 Statistical Inference II
315(60)
10.1 Chi-square tests
315(10)
10.1.1 Testing a distribution
316(2)
10.1.2 Testing a family of distributions
318(3)
10.1.3 Testing independence
321(4)
10.2 Nonparametric statistics
325(15)
10.2.1 Sign test
326(2)
10.2.2 Wilcoxon signed rank test
328(6)
10.2.3 Mann-Whitney-Wilcoxon rank sum test
334(6)
10.3 Bootstrap
340(12)
10.3.1 Bootstrap distribution and all bootstrap samples
340(5)
10.3.2 Computer generated bootstrap samples
345(3)
10.3.3 Bootstrap confidence intervals
348(4)
10.4 Bayesian inference
352(13)
10.4.1 Prior and posterior
353(5)
10.4.2 Bayesian estimation
358(2)
10.4.3 Bayesian credible sets
360(4)
10.4.4 Bayesian hypothesis testing
364(1)
Summary and conclusions
365(1)
Exercises
366(9)
11 Regression
375(42)
11.1 Least squares estimation
376(7)
11.1.1 Examples
376(2)
11.1.2 Method of least squares
378(1)
11.1.3 Linear regression
379(3)
11.1.4 Regression and correlation
382(1)
11.1.5 Overfitting a model
382(1)
11.2 Analysis of variance, prediction, and further inference
383(12)
11.2.1 ANOVA and R-square
383(2)
11.2.2 Tests and confidence intervals
385(6)
11.2.3 Prediction
391(4)
11.3 Multivariate regression
395(9)
11.3.1 Introduction and examples
395(1)
11.3.2 Matrix approach and least squares estimation
396(2)
11.3.3 Analysis of variance, tests, and prediction
398(6)
11.4 Model building
404(8)
11.4.1 Adjusted R-square
404(1)
11.4.2 Extra sum of squares, partial F-tests, and variable selection
405(3)
11.4.3 Categorical predictors and dummy variables
408(4)
Summary and conclusions
412(1)
Exercises
412(5)
Appendix 417(44)
A.1 Data sets
417(2)
A.2 Inventory of distributions
419(7)
A.2.1 Discrete families
420(2)
A.2.2 Continuous families
422(4)
A.3 Distribution tables
426(17)
A.4 Calculus review
443(6)
A.4.1 Inverse function
443(1)
A.4.2 Limits and continuity
443(1)
A.4.3 Sequences and series
444(1)
A.4.4 Derivatives, minimum, and maximum
444(2)
A.4.5 Integrals
446(3)
A.5 Matrices and linear systems
449(6)
A.6 Answers to selected exercises
455(6)
Index 461
Michael Baron is a professor of statistics at the American University in Washington, DC. He has published two books and numerous research articles and book chapters. Dr. Baron is a fellow of the American Statistical Association, a member of the International Society for Bayesian Analysis, and an associate editor of the Journal of Sequential Analysis. In 2007, he was awarded the Abraham Wald Prize in Sequential Analysis. His research focuses on the use of sequential analysis, change-point detection, and Bayesian inference in epidemiology, clinical trials, cyber security, energy, finance, and semiconductor manufacturing. He received a Ph.D. in statistics from the University of Maryland.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97813516974082e.html
Keywords: