Probability and random processes
The second edition enhanced with new chapters, figures, and appendices to cover the new developments in applied mathematical functions This book examines the topics of applied mathematical functions to problems that engineers and researchers solve daily in the course of their work. The text covers s...
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| Format: | eBook Book |
| Language: | English |
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New York
Wiley
2015
John Wiley & Sons, Incorporated Wiley-Blackwell |
| Edition: | 2nd ed |
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| ISBN: | 1118923138, 9781118923139, 1119011906, 9781119011903 |
| Online Access: | Get full text |
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| Abstract | The second edition enhanced with new chapters, figures, and appendices to cover the new developments in applied mathematical functions
This book examines the topics of applied mathematical functions to problems that engineers and researchers solve daily in the course of their work. The text covers set theory, combinatorics, random variables, discrete and continuous probability, distribution functions, convergence of random variables, computer generation of random variates, random processes and stationarity concepts with associated autocovariance and cross covariance functions, estimation theory and Wiener and Kalman filtering ending with two applications of probabilistic methods. Probability tables with nine decimal place accuracy and graphical Fourier transform tables are included for quick reference. The author facilitates understanding of probability concepts for both students and practitioners by presenting over 450 carefully detailed figures and illustrations, and over 350 examples with every step explained clearly and some with multiple solutions.
Additional features of the second edition of Probability and Random Processes are:
* Updated chapters with new sections on Newton-Pepys' problem; Pearson, Spearman, and Kendal correlation coefficients; adaptive estimation techniques; birth and death processes; and renewal processes with generalizations
* A new chapter on Probability Modeling in Teletraffic Engineering written by Kavitha Chandra
* An eighth appendix examining the computation of the roots of discrete probability-generating functions
With new material on theory and applications of probability, Probability and Random Processes, Second Edition is a thorough and comprehensive reference for commonly occurring problems in probabilistic methods and their applications. |
|---|---|
| AbstractList | The second edition enhanced with new chapters, figures, and appendices to cover the new developments in applied mathematical functions This book examines the topics of applied mathematical functions to problems that engineers and researchers solve daily in the course of their work. The text covers set theory, combinatorics, random variables, discrete and continuous probability, distribution functions, convergence of random variables, computer generation of random variates, random processes and stationarity concepts with associated autocovariance and cross covariance functions, estimation theory and Wiener and Kalman filtering ending with two applications of probabilistic methods. Probability tables with nine decimal place accuracy and graphical Fourier transform tables are included for quick reference. The author facilitates understanding of probability concepts for both students and practitioners by presenting over 450 carefully detailed figures and illustrations, and over 350 examples with every step explained clearly and some with multiple solutions. Additional features of the second edition of Probability and Random Processes are: Updated chapters with new sections on Newton-Pepys' problem; Pearson, Spearman, and Kendal correlation coefficients; adaptive estimation techniques; birth and death processes; and renewal processes with generalizations A new chapter on Probability Modeling in Teletraffic Engineering written by Kavitha Chandra An eighth appendix examining the computation of the roots of discrete probability-generating functions With new material on theory and applications of probability, Probability and Random Processes, Second Edition is a thorough and comprehensive reference for commonly occurring problems in probabilistic methods and their applications. The second edition enhanced with new chapters, figures, and appendices to cover the new developments in applied mathematical functions This book examines the topics of applied mathematical functions to problems that engineers and researchers solve daily in the course of their work. The text covers set theory, combinatorics, random variables, discrete and continuous probability, distribution functions, convergence of random variables, computer generation of random variates, random processes and stationarity concepts with associated autocovariance and cross covariance functions, estimation theory and Wiener and Kalman filtering ending with two applications of probabilistic methods. Probability tables with nine decimal place accuracy and graphical Fourier transform tables are included for quick reference. The author facilitates understanding of probability concepts for both students and practitioners by presenting over 450 carefully detailed figures and illustrations, and over 350 examples with every step explained clearly and some with multiple solutions. Additional features of the second edition of Probability and Random Processes are: * Updated chapters with new sections on Newton-Pepys' problem; Pearson, Spearman, and Kendal correlation coefficients; adaptive estimation techniques; birth and death processes; and renewal processes with generalizations * A new chapter on Probability Modeling in Teletraffic Engineering written by Kavitha Chandra * An eighth appendix examining the computation of the roots of discrete probability-generating functions With new material on theory and applications of probability, Probability and Random Processes, Second Edition is a thorough and comprehensive reference for commonly occurring problems in probabilistic methods and their applications. |
| Author | Krishnan, Venkatarama Chandra, Kavitha |
| Author_xml | – sequence: 1 fullname: Krishnan, Venkatarama – sequence: 2 fullname: Chandra, Kavitha |
| BackLink | https://cir.nii.ac.jp/crid/1130282271053641472$$DView record in CiNii |
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| EISBN | 1119011914 9781119011910 1119011906 9781119011903 |
| Edition | 2nd ed 2 1 Second edition. |
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| ISBN | 1118923138 9781118923139 1119011906 9781119011903 |
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| Notes | With contribution from Kavitha Chandra Includes bibliographical references and index |
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| Publisher | Wiley John Wiley & Sons, Incorporated Wiley-Blackwell |
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| Snippet | The second edition enhanced with new chapters, figures, and appendices to cover the new developments in applied mathematical functions
This book examines the... The second edition enhanced with new chapters, figures, and appendices to cover the new developments in applied mathematical functions This book examines the... |
| SourceID | askewsholts proquest perlego nii maruzen |
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| SubjectTerms | Engineering Engineering -- Statistical methods MATHEMATICS Probabilities Science Science -- Statistical methods Statistical methods Stochastic processes |
| TableOfContents | 14.4 Chernoff Bound -- 14.5 Cauchy-Schwartz Inequality -- 14.6 Jensen's Inequality -- 14.7 Convergence Concepts -- 14.8 Limit Theorems -- Chapter 15 Computer Methods for Generating Random Variates -- 15.1 Uniform-Distribution Random Variates -- 15.2 Histograms -- 15.3 Inverse Transformation Techniques -- 15.4 Convolution Techniques -- 15.5 Acceptance-Rejection Techniques -- Chapter 16 Elements of Matrix Algebra -- 16.1 Basic Theory of Matrices -- 16.2 Eigenvalues and Eigenvectors of Matrices -- 16.3 Vector and Matrix Differentiation -- 16.4 Block Matrices -- Chapter 17 Random Vectors and Mean-Square Estimation -- 17.1 Distributions and Densities -- 17.2 Moments of Random Vectors -- 17.3 Vector Gaussian Random Variables -- 17.4 Diagonalization of Covariance Matrices -- 17.5 Simultaneous Diagonalization of Covariance Matrices -- 17.6 Linear Estimation of Vector Variables -- Chapter 18 Estimation Theory -- 18.1 Criteria of Estimators -- 18.2 Estimation of Random Variables -- 18.3 Estimation of Parameters (Point Estimation) -- 18.4 Interval Estimation (Confidence Intervals) -- 18.5 Hypothesis Testing (Binary) -- 18.6 Bayesian Estimation -- Chapter 19 Random Processes -- 19.1 Basic Definitions -- 19.2 Stationary Random Processes -- 19.3 Ergodic Processes -- 19.4 Estimation of Parameters of Random Processes -- 19.4.1 Continuous-Time Processes -- 19.4.2 Discrete-Time Processes -- 19.5 Power Spectral Density -- 19.5.1 Continuous Time -- 19.5.2 Discrete Time -- 19.6 Adaptive Estimation -- Chapter 20 Classification of Random Processes -- 20.1 Specifications of Random Processes -- 20.1.1 Discrete-State Discrete-Time (DSDT) Process -- 20.1.2 Discrete-State Continuous-Time (DSCT) Process -- 20.1.3 Continuous-State Discrete-Time (CSDT) Process -- 20.1.4 Continuous-State Continuous-Time (CSCT) Process -- 20.2 Poisson Process -- 20.3 Binomial Process 7.17 Summary of Distributions of Continuous Random Variables -- Chapter 8 Conditional Densities and Distributions -- 8.1 Conditional Distribution and Density for P{A}≠0 -- 8.2 Conditional Distribution and Density for P{A}=0 -- 8.3 Total Probability and Bayes' Theorem for Densities -- Chapter 9 Joint Densities and Distributions -- 9.1 Joint Discrete Distribution Functions -- 9.2 Joint Continuous Distribution Functions -- 9.3 Bivariate Gaussian Distributions -- Chapter 10 Moments and Conditional Moments -- 10.1 Expectations -- 10.2 Variance -- 10.3 Means and Variances of Some Distributions -- 10.4 Higher-Order Moments -- 10.5 Correlation and Partial Correlation Coefficients -- 10.5.1 Correlation Coefficients -- 10.5.2 Partial Correlation Coefficients -- Chapter 11 Characteristic Functions and Generating Functions -- 11.1 Characteristic Functions -- 11.2 Examples of Characteristic Functions -- 11.3 Generating Functions -- 11.4 Examples of Generating Functions -- 11.5 Moment Generating Functions -- 11.6 Cumulant Generating Functions -- 11.7 Table of Means and Variances -- Chapter 12 Functions of a Single Random Variable -- 12.1 Random Variable g(X) -- 12.2 Distribution of Y=g(X) -- 12.3 Direct Determination of Density fY(y) from fX(x) -- 12.4 Inverse Problem: Finding g(x) given fX(x) and fY(y) -- 12.5 Moments of a Function of a Random Variable -- Chapter 13 Functions of Multiple Random Variables -- 13.1 Function of Two Random Variables, Z=g(X,Y) -- 13.2 Two Functions of Two Random Variables, Z=g(X,Y), W=h(X,Y) -- 13.3 Direct Determination of Joint Density fZW(z,w) from fXY(x,y) -- 13.4 Solving Z=g(X,Y) Using an Auxiliary Random Variable -- 13.5 Multiple Functions of Random Variables -- Chapter 14 Inequalities, Convergences, and Limit Theorems -- 14.1 Degenerate Random Variables -- 14.2 Chebyshev and Allied Inequalities -- 14.3 Markov Inequality 20.4 Independent Increment Process -- 20.5 Random-Walk Process -- 20.6 Gaussian Process -- 20.7 Wiener Process (Brownian Motion) -- 20.8 Markov Process -- 20.9 Markov Chains -- 20.10 Birth and Death Processes -- 20.11 Renewal Processes and Generalizations -- 20.12 Martingale Process -- 20.13 Periodic Random Process -- 20.14 Aperiodic Random Process (Karhunen-Loeve Expansion) -- Chapter 21 Random Processes and Linear Systems -- 21.1 Review of Linear Systems -- 21.2 Random Processes through Linear Systems -- 21.3 Linear Filters -- 21.4 Bandpass Stationary Random Processes -- Chapter 22 Wiener and Kalman Filters -- 22.1 Review of Orthogonality Principle -- 22.2 Wiener Filtering -- 22.3 Discrete Kalman Filter -- 22.4 Continuous Kalman Filter -- Chapter 23 Probability Modeling in Traffic Engineering -- 23.1 Introduction -- 23.2 Teletraffic Models -- 23.3 Blocking Systems -- 23.4 State Probabilities for Systems with Delays -- 23.5 Waiting-Time Distribution for M/M/c/∞ Systems -- 23.6 State Probabilities for M/D/c Systems -- 23.7 Waiting-time distribution for M/D/c/∞ System -- 23.8 Comparison of M/M/c and M/D/c -- References -- Chapter 24 Probabilistic Methods in Transmission Tomography -- 24.1 Introduction -- 24.2 Stochastic Model -- 24.3 Stochastic Estimation Algorithm -- 24.4 Prior Distribution P{M} -- 24.5 Computer Simulation -- 24.6 Results and Conclusions -- 24.7 Discussion of Results -- References -- Appendix -- Appendix A Fourier Transform Tables -- Appendix B Cumulative Gaussian Tables -- Appendix C Inverse Cumulative Gaussian Tables -- Appendix D Inverse Chi-Square Tables -- Appendix E Inverse Student-t Tables -- Appendix F Cumulative Poisson Distribution -- Appendix G Cumulative Binomial Distribution -- Appendix H Computation of Roots of D(Z) = 0 -- References -- Web References -- Index -- EULA Intro -- Title Page -- Copyright Page -- Contents -- Preface for the Second Edition -- Preface for the First Edition -- Chapter 1 Sets, Fields, and Events -- 1.1 Set Definitions -- 1.2 Set Operations -- 1.3 Set Algebras, Fields, and Events -- Chapter 2 Probability Space and Axioms -- 2.1 Probability Space -- 2.2 Conditional Probability -- 2.3 Independence -- 2.4 Total Probability and Bayes´ Theorem -- Chapter 3 Basic Combinatorics -- 3.1 Basic Counting Principles -- 3.2 Permutations -- 3.3 Combinations -- Chapter 4 Discrete Distributions -- 4.1 Bernoulli Trials -- 4.2 Binomial Distribution -- 4.3 Multinomial Distribution -- 4.4 Geometric Distribution -- 4.5 Negative Binomial Distribution -- 4.6 Hypergeometric Distribution -- 4.7 Poisson Distribution -- 4.8 Newton-Pepys Problem and its Extensions -- 4.9 Logarithmic Distribution -- 4.9.1 Finite Law (Benford's Law) -- 4.9.2 Infinite Law -- 4.10 Summary of Discrete Distributions -- Chapter 5 Random Variables -- 5.1 Definition of Random Variables -- 5.2 Determination of Distribution and Density Functions -- 5.3 Properties of Distribution and Density Functions -- 5.4 Distribution Functions from Density Functions -- Chapter 6 Continuous Random Variables and Basic Distributions -- 6.1 Introduction -- 6.2 Uniform Distribution -- 6.3 Exponential Distribution -- 6.4 Normal or Gaussian Distribution -- Chapter 7 Other Continuous Distributions -- 7.1 Introduction -- 7.2 Triangular Distribution -- 7.3 Laplace Distribution -- 7.4 Erlang Distribution -- 7.5 Gamma Distribution -- 7.6 Weibull Distribution -- 7.7 Chi-Square Distribution -- 7.8 Chi and Other Allied Distributions -- 7.9 Student-t DENSITY -- 7.10 Snedecor F Distribution -- 7.11 Lognormal Distribution -- 7.12 Beta Distribution -- 7.13 Cauchy Distribution -- 7.14 Pareto Distribution -- 7.15 Gibbs Distribution -- 7.16 Mixed Distributions 15.2 HISTOGRAMS -- 15.3 INVERSE TRANSFORMATION TECHNIQUES -- 15.4 CONVOLUTION TECHNIQUES -- 15.5 ACCEPTANCE-REJECTION TECHNIQUES -- 16 ELEMENTS OF MATRIX ALGEBRA -- 16.1 BASIC THEORY OF MATRICES -- 16.2 EIGENVALUES AND EIGENVECTORS OF MATRICES -- 16.3 VECTOR AND MATRIX DIFFERENTIATION -- 16.4 BLOCK MATRICES -- 17 RANDOM VECTORS AND MEAN-SQUARE ESTIMATION -- 17.1 DISTRIBUTIONS AND DENSITIES -- 17.2 MOMENTS OF RANDOM VECTORS -- 17.3 VECTOR GAUSSIAN RANDOM VARIABLES -- 17.4 DIAGONALIZATION OF COVARIANCE MATRICES -- 17.5 SIMULTANEOUS DIAGONALIZATION OF COVARIANCE MATRICES -- 17.6 LINEAR ESTIMATION OF VECTOR VARIABLES -- 18 ESTIMATION THEORY -- 18.1 CRITERIA OF ESTIMATORS -- 18.2 ESTIMATION OF RANDOM VARIABLES -- 18.3 ESTIMATION OF PARAMETERS (POINT ESTIMATION) -- 18.4 INTERVAL ESTIMATION (CONFIDENCE INTERVALS) -- 18.5 HYPOTHESIS TESTING (BINARY) -- 18.6 BAYESIAN ESTIMATION -- 19 RANDOM PROCESSES -- 19.1 BASIC DEFINITIONS -- 19.2 STATIONARY RANDOM PROCESSES -- 19.3 ERGODIC PROCESSES -- 19.4 ESTIMATION OF PARAMETERS OF RANDOM PROCESSES -- 19.5 POWER SPECTRAL DENSITY -- 19.6 ADAPTIVE ESTIMATION -- 20 CLASSIFICATION OF RANDOM PROCESSES -- 20.1 SPECIFICATIONS OF RANDOM PROCESSES -- 20.2 POISSON PROCESS -- 20.3 BINOMIAL PROCESS -- 20.4 INDEPENDENT INCREMENT PROCESS -- 20.5 RANDOM-WALK PROCESS -- 20.6 GAUSSIAN PROCESS -- 20.7 WIENER PROCESS (BROWNIAN MOTION) -- 20.8 MARKOV PROCESS -- 20.9 MARKOV CHAINS -- 20.10 BIRTH AND DEATH PROCESSES -- 20.11 RENEWAL PROCESSES AND GENERALIZATIONS -- 20.12 MARTINGALE PROCESS -- 20.13 PERIODIC RANDOM PROCESS -- 20.14 APERIODIC RANDOM PROCESS (KARHUNEN-LOEVE EXPANSION) -- 21 RANDOM PROCESSES AND LINEAR SYSTEMS -- 21.1 REVIEW OF LINEAR SYSTEMS -- 21.2 RANDOM PROCESSES THROUGH LINEAR SYSTEMS -- 21.3 LINEAR FILTERS -- 21.4 BANDPASS STATIONARY RANDOM PROCESSES -- 22 WIENER AND KALMAN FILTERS 8.1 CONDITIONAL DISTRIBUTION AND DENSITY FOR P{A} -- 8.2 CONDITIONAL DISTRIBUTION AND DENSITY FOR P{A} = 0 -- 8.3 TOTAL PROBABILITY AND BAYES' THEOREM FOR DENSITIES -- 9 JOINT DENSITIES AND DISTRIBUTIONS -- 9.1 JOINT DISCRETE DISTRIBUTION FUNCTIONS -- 9.2 JOINT CONTINUOUS DISTRIBUTION FUNCTIONS -- 9.3 BIVARIATE GAUSSIAN DISTRIBUTIONS -- 10 MOMENTS AND CONDITIONAL MOMENTS -- 10.1 EXPECTATIONS -- 10.2 VARIANCE -- 10.3 MEANS AND VARIANCES OF SOME DISTRIBUTIONS -- 10.4 HIGHER-ORDER MOMENTS -- 10.5 CORRELATION AND PARTIAL CORRELATION COEFFICIENTS -- 11 CHARACTERISTIC FUNCTIONS AND GENERATING FUNCTIONS -- 11.1 CHARACTERISTIC FUNCTIONS -- 11.2 EXAMPLES OF CHARACTERISTIC FUNCTIONS -- 11.3 GENERATING FUNCTIONS -- 11.4 EXAMPLES OF GENERATING FUNCTIONS -- 11.5 MOMENT GENERATING FUNCTIONS -- 11.6 CUMULANT GENERATING FUNCTIONS -- 11.7 TABLE OF MEANS AND VARIANCES -- 12 FUNCTIONS OF A SINGLE RANDOM VARIABLE -- 12.1 RANDOM VARIABLE g(X) -- 12.2 DISTRIBUTION OF Y = g(X) -- 12.3 DIRECT DETERMINATION OF DENSITY fY(y) from fX(x) -- 12.4 INVERSE PROBLEM: FINDING g(x) GIVEN fX(x) AND fY(y) -- 12.5 MOMENTS OF A FUNCTION OF A RANDOM VARIABLE -- 13 FUNCTIONS OF MULTIPLE RANDOM VARIABLES -- 13.1 FUNCTION OF TWO RANDOM VARIABLES, Z = g(X,Y) -- 13.2 TWO FUNCTIONS OF TWO RANDOM VARIABLES, Z = g(X,Y), W = h(X,Y) -- 13.3 DIRECT DETERMINATION OF JOINT DENSITY fZW(z,w) FROM fXY(x,y) -- 13.4 SOLVING Z = g(X,Y) USING AN AUXILIARY RANDOM VARIABLE -- 13.5 MULTIPLE FUNCTIONS OF RANDOM VARIABLES -- 14 INEQUALITIES, CONVERGENCES, AND LIMIT THEOREMS -- 14.1 DEGENERATE RANDOM VARIABLES -- 14.2 CHEBYSHEV AND ALLIED INEQUALITIES -- 14.3 MARKOV INEQUALITY -- 14.4 CHERNOFF BOUND -- 14.5 CAUCHY-SCHWARTZ INEQUALITY -- 14.6 JENSEN'S INEQUALITY -- 14.7 CONVERGENCE CONCEPTS -- 14.8 LIMIT THEOREMS -- 15 COMPUTER METHODS FOR GENERATING RANDOM VARIATES -- 15.1 UNIFORM-DISTRIBUTION RANDOM VARIATES Intro -- TITLE PAGE -- TABLE OF CONTENTS -- PREFACE FOR THE SECOND EDITION -- PREFACE FOR THE FIRST EDITION -- 1 SETS, FIELDS, AND EVENTS -- 1.1 SET DEFINITIONS -- 1.2 SET OPERATIONS -- 1.3 SET ALGEBRAS, FIELDS, AND EVENTS -- 2 PROBABILITY SPACE AND AXIOMS -- 2.1 PROBABILITY SPACE -- 2.2 CONDITIONAL PROBABILITY -- 2.3 INDEPENDENCE -- 2.4 TOTAL PROBABILITY AND BAYES' THEOREM -- 3 BASIC COMBINATORICS -- 3.1 BASIC COUNTING PRINCIPLES -- 3.2 PERMUTATIONS -- 3.3 COMBINATIONS -- 4 DISCRETE DISTRIBUTIONS -- 4.1 BERNOULLI TRIALS -- 4.2 BINOMIAL DISTRIBUTION -- 4.3 MULTINOMIAL DISTRIBUTION -- 4.4 GEOMETRIC DISTRIBUTION -- 4.5 NEGATIVE BINOMIAL DISTRIBUTION -- 4.6 HYPERGEOMETRIC DISTRIBUTION -- 4.7 POISSON DISTRIBUTION -- 4.8 NEWTON-PEPYS PROBLEM AND ITS EXTENSIONS -- 4.9 LOGARITHMIC DISTRIBUTION -- 4.10 SUMMARY OF DISCRETE DISTRIBUTIONS -- 5 RANDOM VARIABLES -- 5.1 DEFINITION OF RANDOM VARIABLES -- 5.2 DETERMINATION OF DISTRIBUTION AND DENSITY FUNCTIONS -- 5.3 PROPERTIES OF DISTRIBUTION AND DENSITY FUNCTIONS -- 5.4 DISTRIBUTION FUNCTIONS FROM DENSITY FUNCTIONS -- 6 CONTINUOUS RANDOM VARIABLES AND BASIC DISTRIBUTIONS -- 6.1 INTRODUCTION -- 6.2 UNIFORM DISTRIBUTION -- 6.3 EXPONENTIAL DISTRIBUTION -- 6.4 NORMAL OR GAUSSIAN DISTRIBUTION -- 7 OTHER CONTINUOUS DISTRIBUTIONS -- 7.1 INTRODUCTION -- 7.2 TRIANGULAR DISTRIBUTION -- 7.3 LAPLACE DISTRIBUTION -- 7.4 ERLANG DISTRIBUTION -- 7.5 GAMMA DISTRIBUTION -- 7.6 WEIBULL DISTRIBUTION -- 7.7 CHI-SQUARE DISTRIBUTION -- 7.8 CHI AND OTHER ALLIED DISTRIBUTIONS -- 7.9 STUDENT-t DENSITY -- 7.10 SNEDECOR F DISTRIBUTION -- 7.11 LOGNORMAL DISTRIBUTION -- 7.12 BETA DISTRIBUTION -- 7.13 CAUCHY DISTRIBUTION -- 7.14 PARETO DISTRIBUTION -- 7.15 GIBBS DISTRIBUTION -- 7.16 MIXED DISTRIBUTIONS -- 7.17 SUMMARY OF DISTRIBUTIONS OF CONTINUOUS RANDOM VARIABLES -- 8 CONDITIONAL DENSITIES AND DISTRIBUTIONS 22.1 REVIEW OF ORTHOGONALITY PRINCIPLE -- 22.2 WIENER FILTERING -- 22.3 DISCRETE KALMAN FILTER1 -- 22.4 CONTINUOUS KALMAN FILTER -- 23 PROBABILITY MODELING IN TRAFFIC ENGINEERING -- 23.1 INTRODUCTION -- 23.2 TELETRAFFIC MODELS -- 23.3 BLOCKING SYSTEMS -- 23.4 STATE PROBABILITIES FOR SYSTEMS WITH DELAYS -- 23.5 WAITING-TIME DISTRIBUTION FOR M/M/c/∞ SYSTEMS -- 23.6 STATE PROBABILITIES FOR M/D/c SYSTEMS -- 23.7 WAITING-TIME DISTRIBUTION FOR M/D/c/∞ SYSTEM -- 23.8 COMPARISON OF M/M/c AND M/D/c -- REFERENCES -- 24 PROBABILISTIC METHODS IN TRANSMISSION TOMOGRAPHY -- 24.1 INTRODUCTION -- 24.2 STOCHASTIC MODEL -- 24.3 STOCHASTIC ESTIMATION ALGORITHM -- 24.4 PRIOR DISTRIBUTION P{} -- 24.5 COMPUTER SIMULATION -- 24.6 RESULTS AND CONCLUSIONS -- 24.7 DISCUSSION OF RESULTS -- REFERENCES -- APPENDIX A A FOURIER TRANSFORM TABLES -- APPENDIX B CUMULATIVE GAUSSIAN TABLES -- APPENDIX C INVERSE CUMULATIVE GAUSSIAN TABLES -- APPENDIX D INVERSE CHI-SQUARE TABLES -- APPENDIX E INVERSE STUDENT-t TABLES -- APPENDIX F CUMULATIVE POISSON DISTRIBUTION -- APPENDIX G CUMULATIVE BINOMIAL DISTRIBUTION -- APPENDIX H COMPUTATION OF ROOTS OF D(z) = 0 -- REFERENCES -- WEB REFERENCES -- INDEX -- END USER LICENSE AGREEMENT |
| Title | Probability and random processes |
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