000 07562nam a2200625Ki 4500
001 on1134770019
003 OCoLC
005 20220711203555.0
006 m o d
007 cr cnu---unuuu
008 200103s2009 njua ob 001 0 eng d
040 _aDG1
_beng
_erda
_epn
_cDG1
020 _a9781119536963
_q(electronic bk.)
020 _a1119536960
_q(electronic bk.)
020 _z9780470280737
020 _z0470280735
029 1 _aAU@
_b000066461266
035 _a(OCoLC)1134770019
050 4 _aQA76.9.M35
_bH863 2009eb
082 0 4 _a004.01/5113
_222
084 _aST 601 R01
_2rvk
084 _aST 601
_2rvk
084 _aSK 820
_2rvk
084 _a*60-01
_2msc
084 _a31.70
_2bcl
084 _a60-04
_2msc
084 _a83.03
_2bcl
084 _a85.20
_2bcl
084 _aQH 170
_2rvk
049 _aMAIN
100 1 _aHorgan, Jane M.,
_d1947-
_98933
245 1 0 _aProbability with R :
_ban introduction with computer science applications /
_cJane M. Horgan.
264 1 _aHoboken, N.J. :
_bWiley,
_c©2009.
300 _a1 online resource (xviii, 393 pages) :
_billustrations.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
504 _aIncludes bibliographical references and index.
505 0 _aPreface. I. THE R LANGUAGE. 1. Basics of R.1.1 What is R?1.2 Installing R.1.3 R Documentation. 1.4 Basics. 1.5 Getting Help. 1.6 Data Entry. 1.7 Tidying Up. 1.8 Saving and Retrieving the Workspace. 2. Summarising Statistical Data. 2.1 Measures of Central Tendency. 2.2 Measures of Dispersion. 2.3 Overall Summary Statistics. 2.4 Programming in R.3. Graphical Displays. 3.1 Boxplots. 3.2 Histograms. 3.3 Stem and Leaf. 3.4 Scatter Plots. 3.5 Graphical Display vs Summary Statistics. II: FUNDAMENTALS OF PROBABILITY. 4. Basics. 4.1 Experiments, Sample Spaces and Events. 4.2 Classical Approach to Probability. 4.3 Permutations and Combinations. 4.4 The Birthday Problem. 4.5 Balls and Bins. 4.6 Relative Frequency Approach to Probability. 4.7 Simulating Probabilities. 5. Rules of Probability. 5.1 Probability and Sets. 5.2 Mutually Exclusive Events. 5.3 Complementary Events. 5.4 Axioms of Probability. 5.5 Properties of Probability. 6. Conditional Probability. 6.1 Multiplication Law of Probability. 6.2 Independent Events. 6.3 The Intel Fiasco. 6.4 Law of Total Probability. 6.5 Trees. 7. Posterior Probability and Bayes. 7.1 Bayes' Rule. 7.2 Hardware Fault Diagnosis. 7.3 Machine Learning. 7.4 The Fundamental Equation of Machine Translation. 8. Reliability. 8.1 Series Systems. 8.2 Parallel Systems. 8.3 Reliability of a System. 8.4 Series-Parallel Systems. 8.5 The Design of Systems. 8.6 The General System. III: DISCRETE DISTRIBUTIONS. 9. Discrete Distributions. 9.1 Discrete Random Variables. 9.2 Cumulative Distribution Function. 9.3 Some Simple Discrete Distributions. 9.4 Benford's Law. 9.5 Summarising Random Variables: Expectation. 9.6 Properties of Expectations. 9.7 Simulating Expectation for Discrete Random Variables. 10. The Geometric Distribution. 10.1 Geometric Random Variables. 10.2 Cumulative Distribution Function. 10.3 The Quantile Function. 10.4 Geometric Expectations. 10.5 Simulating Geometric Probabilities and Expectations. 10.6 Amnesia. 10.7 Project. 11. The Binomial Distribution. 11.1 Binomial Probabilities. 11.2 Binomial Random Variables. 11.3 Cumulative Distribution Function. 11.4 The Quantile Function. 11.5 Machine Learning and the Binomial Distribution. 11.6 Binomial Expectations. 11.7 Simulating Binomial Probabilities and Expectations. 11.8 Project. 12. The Hypergeometric Distribution. 12.1 Hypergeometric Random Variables. 12.2 Cumulative Distribution Function. 12.3 The Lottery. 12.4 Hypergeometric or Binomial?.12.5 Project. 13. The Poisson Distribution. 13.1 Death by Horse Kick. 13.2 Limiting Binomial Distribution. 13.3 Random Events in Time and Space. 13.4 Probability Density Function. 13.5 Cumulative Distribution Function. 13.6 The Quantile Function. 13.7 Estimating Software Reliability. 13.8 Modelling Defects in Integrated Circuits. 13.9 Simulating Poisson Probabilities. 13.10Projects. 14. Sampling Inspection Schemes. 14.1 Introduction. 14.2 Single Sampling Inspection Schemes. 14.3 Acceptance Probabilities. 14.4 Simulating Sampling Inspections Schemes. 14.5 Operating Characteristic Curve. 14.6 Producer's and Consumer's Risks. 14.7 Design of Sampling Schemes. 14.8 Rectifying Sampling Inspection Schemes. 14.9 Average Outgoing Quality. 14.10Double Sampling Inspection Schemes. 14.11Average Sample Size. 14.12Single vs Double Schemes. 14.13Project. IV. CONTINUOUS DISTRIBUTIONS. 15. Continuous Distributions. 15.1 Continuous Random Variables. 15.2 Probability Density Function. 15.3 Cumulative Distribution Function. 15.4 The Uniform Distribution. 15.5 Expectation of a Continuous Random Variable. 15.6 Simulating Continuous Variables. 16. The Exponential Distribution. 16.1 Probability Density Function Of Waiting Times. 16.2 Cumulative Distribution Function. 16.3 Quantiles. 16.4 Exponential Expectations. 16.5 Simulating the Exponential Distribution. 16.6 Amnesia. 16.7 Simulating Markov. 17. Applications of the Exponential Distribution. 17.1 Failure Rate and Reliability. 17.2 Modelling Response Times. 17.3 Queue Lengths. 17.4 Average Response Time. 17.5 Extensions of the M/M/1 queue. 18. The Normal Distribution. 18.1 The Normal Probability Density Function. 18.2 The Cumulative Distribution Function. 18.3 Quantiles. 18.4 The Standard Normal Distribution. 18.5 Achieving Normality; Limiting Distributions. 18.6 Project in R.19. Process Control. 19.1 Control Charts. 19.2 Cusum Charts. 19.3 Charts for Defective Rates. 19.4 Project. V. TAILING OFF. 20. Markov and Chebyshev Bound. 20.1 Markov's Inequality. 20.2 Algorithm Run-Time. 20.3 Chebyshev's Inequality. Appendix 1: Variance derivations. Appendix 2: Binomial approximation to the hypergeometric. Appendix 3:. Standard Normal Tables.
520 1 _a"Probability with R serves as a comprehensive and introductory book on probability with an emphasis on computing-related applications. Real examples show how probability can be used in practical situations, and the freely available and downloadable statistical programming language R illustrates and clarifies the book's main principles." "With its accessible and hands-on approach, Probability with R is an ideal book for a first course in probability at the upper-undergraduate and graduate levels for readers with a background in computer science, engineering, and the general sciences. It also serves as a valuable reference for computing professionals who would like to further understand the relevance of probability in their areas of practice."--Jacket.
588 0 _aPrint version record.
650 0 _aComputer science
_xMathematics.
_93866
650 0 _aProbabilities.
_94604
650 0 _aR (Computer program language)
_94991
650 7 _aComputer science
_xMathematics.
_2fast
_0(OCoLC)fst00872460
_93866
650 7 _aProbabilities.
_2fast
_0(OCoLC)fst01077737
_94604
650 7 _aR (Computer program language)
_2fast
_0(OCoLC)fst01086207
_94991
650 7 _aDatavetenskap matematik.
_2sao
_98934
650 7 _aSannolikhet.
_2sao
_98935
655 4 _aElectronic books.
_93294
776 0 8 _iPrint version:
_aHorgan, Jane M., 1947-
_tProbability with R.
_dHoboken, N.J. : Wiley, ©2009
_z9780470280737
_w(DLC) 2008022817
_w(OCoLC)228701418
856 4 0 _uhttps://doi.org/10.1002/9781119536963
_zWiley Online Library
942 _cEBK
994 _aC0
_bDG1
999 _c69241
_d69241