{"product_id":"probability-with-r-9781119536949","title":"Probability with R","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eProvides a comprehensive introduction to probability with an emphasis on computing-related applications\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThis self-contained new and extended edition outlines a first course in probability applied to computer-related disciplines. As in the first edition, experimentation and simulation are favoured over mathematical proofs. The freely down-loadable statistical programming language\u003ci\u003eR\u003c\/i\u003eis used throughout the text, not only as a tool for calculation and data analysis, but also to illustrate concepts of probability and to simulate distributions. The examples in\u003ci\u003eProbability with R: An Introduction with Computer Science Applications, Second Edition\u003c\/i\u003ecover a wide range of computer science applications, including: testing program performance; measuring response time and CPU time; estimating the reliability of components and systems; evaluating algorithms and queuing systems.\u003c\/p\u003e \u003cp\u003eChapters cover: The R language; summarizing statistical data; graphical displays; the fund\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003ePreface to the Second Edition xiii\u003c\/p\u003e \u003cp\u003ePreface to the First Edition xvii\u003c\/p\u003e \u003cp\u003eAcknowledgments xxi\u003c\/p\u003e \u003cp\u003eAbout the Companion Website xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eI The \u003ci\u003eR \u003c\/i\u003eLanguage 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Basics of \u003ci\u003eR \u003c\/i\u003e3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 What is \u003ci\u003eR\u003c\/i\u003e? 3\u003c\/p\u003e \u003cp\u003e1.2 Installing \u003ci\u003eR \u003c\/i\u003e4\u003c\/p\u003e \u003cp\u003e1.3 \u003ci\u003eR \u003c\/i\u003eDocumentation 4\u003c\/p\u003e \u003cp\u003e1.4 Basics 5\u003c\/p\u003e \u003cp\u003e1.5 Getting Help 6\u003c\/p\u003e \u003cp\u003e1.6 Data Entry 7\u003c\/p\u003e \u003cp\u003e1.7 Missing Values 11\u003c\/p\u003e \u003cp\u003e1.8 Editing 12\u003c\/p\u003e \u003cp\u003e1.9 Tidying Up 12\u003c\/p\u003e \u003cp\u003e1.10 Saving and Retrieving 13\u003c\/p\u003e \u003cp\u003e1.11 Packages 13\u003c\/p\u003e \u003cp\u003e1.12 Interfaces 14\u003c\/p\u003e \u003cp\u003e1.13 Project 16\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Summarizing Statistical Data 17\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Measures of Central Tendency 17\u003c\/p\u003e \u003cp\u003e2.2 Measures of Dispersion 21\u003c\/p\u003e \u003cp\u003e2.3 Overall Summary Statistics 24\u003c\/p\u003e \u003cp\u003e2.4 Programming in \u003ci\u003eR \u003c\/i\u003e25\u003c\/p\u003e \u003cp\u003e2.5 Project 30\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Graphical Displays 31\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Boxplots 31\u003c\/p\u003e \u003cp\u003e3.2 Histograms 36\u003c\/p\u003e \u003cp\u003e3.3 Stem and Leaf 40\u003c\/p\u003e \u003cp\u003e3.4 Scatter Plots 40\u003c\/p\u003e \u003cp\u003e3.5 The Line of Best Fit 43\u003c\/p\u003e \u003cp\u003e3.6 Machine Learning and the Line of Best Fit 44\u003c\/p\u003e \u003cp\u003e3.7 Graphical Displays Versus Summary Statistics 49\u003c\/p\u003e \u003cp\u003e3.8 Projects 53\u003c\/p\u003e \u003cp\u003e\u003cb\u003eII Fundamentals of Probability 55\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Probability Basics 57\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Experiments, Sample Spaces, and Events 58\u003c\/p\u003e \u003cp\u003e4.2 Classical Approach to Probability 61\u003c\/p\u003e \u003cp\u003e4.3 Permutations and Combinations 64\u003c\/p\u003e \u003cp\u003e4.4 The Birthday Problem 71\u003c\/p\u003e \u003cp\u003e4.5 Balls and Bins 76\u003c\/p\u003e \u003cp\u003e4.6 \u003ci\u003eR \u003c\/i\u003eFunctions for Allocation 79\u003c\/p\u003e \u003cp\u003e4.7 Allocation Overload 81\u003c\/p\u003e \u003cp\u003e4.8 Relative Frequency Approach to Probability 83\u003c\/p\u003e \u003cp\u003e4.9 Simulating Probabilities 84\u003c\/p\u003e \u003cp\u003e4.10 Projects 89\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Rules of Probability 91\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Probability and Sets 91\u003c\/p\u003e \u003cp\u003e5.2 Mutually Exclusive Events 92\u003c\/p\u003e \u003cp\u003e5.3 Complementary Events 93\u003c\/p\u003e \u003cp\u003e5.4 Axioms of Probability 94\u003c\/p\u003e \u003cp\u003e5.5 Properties of Probability 96\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Conditional Probability 104\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Multiplication Law of Probability 107\u003c\/p\u003e \u003cp\u003e6.2 Independent Events 108\u003c\/p\u003e \u003cp\u003e6.3 Independence of More than Two Events 110\u003c\/p\u003e \u003cp\u003e6.4 The Intel Fiasco 113\u003c\/p\u003e \u003cp\u003e6.5 Law of Total Probability 115\u003c\/p\u003e \u003cp\u003e6.6 Trees 118\u003c\/p\u003e \u003cp\u003e6.7 Project 123\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Posterior Probability and Bayes 124\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Bayes’ Rule 124\u003c\/p\u003e \u003cp\u003e7.2 Hardware Fault Diagnosis 131\u003c\/p\u003e \u003cp\u003e7.3 Machine Learning and Classification 132\u003c\/p\u003e \u003cp\u003e7.4 Spam Filtering 135\u003c\/p\u003e \u003cp\u003e7.5 Machine Translation 137\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Reliability 142\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Series Systems 142\u003c\/p\u003e \u003cp\u003e8.2 Parallel Systems 143\u003c\/p\u003e \u003cp\u003e8.3 Reliability of a System 143\u003c\/p\u003e \u003cp\u003e8.4 Series–Parallel Systems 150\u003c\/p\u003e \u003cp\u003e8.5 The Design of Systems 153\u003c\/p\u003e \u003cp\u003e8.6 The General System 158\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIII Discrete Distributions 161\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Introduction to Discrete Distributions 163\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Discrete Random Variables 163\u003c\/p\u003e \u003cp\u003e9.2 Cumulative Distribution Function 168\u003c\/p\u003e \u003cp\u003e9.3 Some Simple Discrete Distributions 170\u003c\/p\u003e \u003cp\u003e9.4 Benford’s Law 174\u003c\/p\u003e \u003cp\u003e9.5 Summarizing Random Variables: Expectation 175\u003c\/p\u003e \u003cp\u003e9.6 Properties of Expectations 180\u003c\/p\u003e \u003cp\u003e9.7 Simulating Discrete Random Variables and Expectations 183\u003c\/p\u003e \u003cp\u003e9.8 Bivariate Distributions 187\u003c\/p\u003e \u003cp\u003e9.9 Marginal Distributions 189\u003c\/p\u003e \u003cp\u003e9.10 Conditional Distributions 190\u003c\/p\u003e \u003cp\u003e9.11 Project 194\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 The Geometric Distribution 196\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Geometric Random Variables 198\u003c\/p\u003e \u003cp\u003e10.2 Cumulative Distribution Function 203\u003c\/p\u003e \u003cp\u003e10.3 The Quantile Function 207\u003c\/p\u003e \u003cp\u003e10.4 Geometric Expectations 209\u003c\/p\u003e \u003cp\u003e10.5 Simulating Geometric Probabilities and Expectations 210\u003c\/p\u003e \u003cp\u003e10.6 Amnesia 217\u003c\/p\u003e \u003cp\u003e10.7 Simulating Markov 219\u003c\/p\u003e \u003cp\u003e10.8 Projects 224\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 The Binomial Distribution 226\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Binomial Probabilities 227\u003c\/p\u003e \u003cp\u003e11.2 Binomial Random Variables 229\u003c\/p\u003e \u003cp\u003e11.3 Cumulative Distribution Function 233\u003c\/p\u003e \u003cp\u003e11.4 The Quantile Function 235\u003c\/p\u003e \u003cp\u003e11.5 Reliability: The General System 238\u003c\/p\u003e \u003cp\u003e11.6 Machine Learning 241\u003c\/p\u003e \u003cp\u003e11.7 Binomial Expectations 245\u003c\/p\u003e \u003cp\u003e11.8 Simulating Binomial Probabilities and Expectations 248\u003c\/p\u003e \u003cp\u003e11.9 Projects 254\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 The Hypergeometric Distribution 255\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Hypergeometric Random Variables 257\u003c\/p\u003e \u003cp\u003e12.2 Cumulative Distribution Function 260\u003c\/p\u003e \u003cp\u003e12.3 The Lottery 262\u003c\/p\u003e \u003cp\u003e12.4 Hypergeometric or Binomial? 266\u003c\/p\u003e \u003cp\u003e12.5 Projects 273\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 The Poisson Distribution 274\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Death by Horse Kick 274\u003c\/p\u003e \u003cp\u003e13.2 Limiting Binomial Distribution 275\u003c\/p\u003e \u003cp\u003e13.3 Random Events in Time and Space 281\u003c\/p\u003e \u003cp\u003e13.4 Probability Density Function 283\u003c\/p\u003e \u003cp\u003e13.5 Cumulative Distribution Function 287\u003c\/p\u003e \u003cp\u003e13.6 The Quantile Function 289\u003c\/p\u003e \u003cp\u003e13.7 Estimating Software Reliability 290\u003c\/p\u003e \u003cp\u003e13.8 Modeling Defects in Integrated Circuits 292\u003c\/p\u003e \u003cp\u003e13.9 Simulating Poisson Probabilities 293\u003c\/p\u003e \u003cp\u003e13.10 Projects 298\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Sampling Inspection Schemes 299\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 299\u003c\/p\u003e \u003cp\u003e14.2 Single Sampling Inspection Schemes 300\u003c\/p\u003e \u003cp\u003e14.3 Acceptance Probabilities 301\u003c\/p\u003e \u003cp\u003e14.4 Simulating Sampling Inspection Schemes 303\u003c\/p\u003e \u003cp\u003e14.5 Operating Characteristic Curve 308\u003c\/p\u003e \u003cp\u003e14.6 Producer’s and Consumer’s Risks 310\u003c\/p\u003e \u003cp\u003e14.7 Design of Sampling Schemes 311\u003c\/p\u003e \u003cp\u003e14.8 Rectifying Sampling Inspection Schemes 315\u003c\/p\u003e \u003cp\u003e14.9 Average Outgoing Quality 316\u003c\/p\u003e \u003cp\u003e14.10 Double Sampling Inspection Schemes 318\u003c\/p\u003e \u003cp\u003e14.11 Average Sample Size 319\u003c\/p\u003e \u003cp\u003e14.12 Single Versus Double Schemes 320\u003c\/p\u003e \u003cp\u003e14.13 Projects 324\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIV Continuous Distributions 325\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Introduction to Continuous Distributions 327\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction to Continuous Random Variables 328\u003c\/p\u003e \u003cp\u003e15.2 Probability Density Function 328\u003c\/p\u003e \u003cp\u003e15.3 Cumulative Distribution Function 331\u003c\/p\u003e \u003cp\u003e15.4 The Uniform Distribution 332\u003c\/p\u003e \u003cp\u003e15.5 Expectation of a Continuous Random Variable 336\u003c\/p\u003e \u003cp\u003e15.6 Simulating Continuous Variables 338\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 The Exponential Distribution 341\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Modeling Waiting Times 341\u003c\/p\u003e \u003cp\u003e16.2 Probability Density Function of Waiting Times 342\u003c\/p\u003e \u003cp\u003e16.3 Cumulative Distribution Function 344\u003c\/p\u003e \u003cp\u003e16.4 Modeling Lifetimes 347\u003c\/p\u003e \u003cp\u003e16.5 Quantiles 349\u003c\/p\u003e \u003cp\u003e16.6 Exponential Expectations 351\u003c\/p\u003e \u003cp\u003e16.7 Simulating Exponential Probabilities and Expectations 353\u003c\/p\u003e \u003cp\u003e16.8 Amnesia 356\u003c\/p\u003e \u003cp\u003e16.9 Simulating Markov 360\u003c\/p\u003e \u003cp\u003e16.10 Project 369\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Queues 370\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 The Single Server Queue 370\u003c\/p\u003e \u003cp\u003e17.2 Traffic Intensity 371\u003c\/p\u003e \u003cp\u003e17.3 Queue Length 372\u003c\/p\u003e \u003cp\u003e17.4 Average Response Time 376\u003c\/p\u003e \u003cp\u003e17.5 Extensions of the M\/M\/1 Queue 378\u003c\/p\u003e \u003cp\u003e17.6 Project 382\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 The Normal Distribution 383\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 The Normal Probability Density Function 385\u003c\/p\u003e \u003cp\u003e18.2 The Cumulative Distribution Function 387\u003c\/p\u003e \u003cp\u003e18.3 Quantiles 389\u003c\/p\u003e \u003cp\u003e18.4 The Standard Normal Distribution 391\u003c\/p\u003e \u003cp\u003e18.5 Achieving Normality: Limiting Distributions 394\u003c\/p\u003e \u003cp\u003e18.6 Projects 405\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Process Control 407\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Control Charts 407\u003c\/p\u003e \u003cp\u003e19.2 Cusum Charts 411\u003c\/p\u003e \u003cp\u003e19.3 Charts for Defective Rates 412\u003c\/p\u003e \u003cp\u003e19.4 Project 416\u003c\/p\u003e \u003cp\u003e\u003cb\u003eV Tailing Off 417\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 The Inequalities of Markov and Chebyshev 419\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 Markov’s Inequality 420\u003c\/p\u003e \u003cp\u003e20.2 Algorithm Runtime 426\u003c\/p\u003e \u003cp\u003e20.3 Chebyshev’s Inequality 427\u003c\/p\u003e \u003cp\u003eAppendix A: Data: Examination Results 433\u003c\/p\u003e \u003cp\u003eAppendix B: The Line of Best Fit: Coefficient Derivations 437\u003c\/p\u003e \u003cp\u003eAppendix C: Variance Derivations 440\u003c\/p\u003e \u003cp\u003eAppendix D: Binomial Approximation to the Hypergeometric 446\u003c\/p\u003e \u003cp\u003eAppendix E: Normal Tables 448\u003c\/p\u003e \u003cp\u003eAppendix F: The Inequalities of Markov and Chebyshev 450\u003c\/p\u003e \u003cp\u003eIndex to \u003ci\u003eR \u003c\/i\u003eCommands 453\u003c\/p\u003e \u003cp\u003eIndex 457\u003c\/p\u003e \u003cp\u003ePostface\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407075811671,"sku":"9781119536949","price":85.46,"currency_code":"GBP","in_stock":false}],"url":"https:\/\/bookcurl.com\/products\/probability-with-r-9781119536949","provider":"Book Curl","version":"1.0","type":"link"}