Probability, random variables, and random processes theory and signal processing applications
Probability, Random Variables, and Random Processes is a comprehensive textbook on probability theory for engineers that provides a more rigorous mathematical framework than is usually encountered in undergraduate courses. It is intended for first-year graduate students who have some familiarity wit...
Uloženo v:
| Hlavní autor: | |
|---|---|
| Médium: | E-kniha Kniha |
| Jazyk: | angličtina |
| Vydáno: |
Hoboken, NJ
Wiley
2012
John Wiley & Sons, Incorporated Wiley-Interscience Wiley-Blackwell John Wiley & Sons |
| Vydání: | 1st ed. |
| Témata: | |
| ISBN: | 1118393945, 9781118393949, 0470242094, 9780470242094, 9781118393956, 1118393953 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Probability, Random Variables, and Random Processes is a comprehensive textbook on probability theory for engineers that provides a more rigorous mathematical framework than is usually encountered in undergraduate courses. It is intended for first-year graduate students who have some familiarity with probability and random variables, though not necessarily of random processes and systems that operate on random signals. It is also appropriate for advanced undergraduate students who have a strong mathematical background. The book has the following features: Several appendices include related material on integration, important inequalities and identities, frequency-domain transforms, and linear algebra. These topics have been included so that the book is relatively self-contained. One appendix contains an extensive summary of 33 random variables and their properties such as moments, characteristic functions, and entropy. Unlike most books on probability, numerous figures have been included to clarify and expand upon important points. Over 600 illustrations and MATLAB plots have been designed to reinforce the material and illustrate the various characterizations and properties of random quantities. Sufficient statistics are covered in detail, as is their connection to parameter estimation techniques. These include classical Bayesian estimation and several optimality criteria: mean-square error, mean-absolute error, maximum likelihood, method of moments, and least squares. The last four chapters provide an introduction to several topics usually studied in subsequent engineering courses: communication systems and information theory; optimal filtering (Wiener and Kalman); adaptive filtering (FIR and IIR); and antenna beamforming, channel equalization, and direction finding. This material is available electronically at the companion website. Probability, Random Variables, and Random Processes is the only textbook on probability for engineers that includes relevant background material, provides extensive summaries of key results, and extends various statistical techniques to a range of applications in signal processing. |
|---|---|
| AbstractList | Probability, Random Variables, and Random Processes is a comprehensive textbook on probability theory for engineers that provides a more rigorous mathematical framework than is usually encountered in undergraduate courses. It is intended for first-year graduate students who have some familiarity with probability and random variables, though not necessarily of random processes and systems that operate on random signals. It is also appropriate for advanced undergraduate students who have a strong mathematical background. The book has the following features: Several appendices include related material on integration, important inequalities and identities, frequency-domain transforms, and linear algebra. These topics have been included so that the book is relatively self-contained. One appendix contains an extensive summary of 33 random variables and their properties such as moments, characteristic functions, and entropy. Unlike most books on probability, numerous figures have been included to clarify and expand upon important points. Over 600 illustrations and MATLAB plots have been designed to reinforce the material and illustrate the various characterizations and properties of random quantities. Sufficient statistics are covered in detail, as is their connection to parameter estimation techniques. These include classical Bayesian estimation and several optimality criteria: mean-square error, mean-absolute error, maximum likelihood, method of moments, and least squares. The last four chapters provide an introduction to several topics usually studied in subsequent engineering courses: communication systems and information theory; optimal filtering (Wiener and Kalman); adaptive filtering (FIR and IIR); and antenna beamforming, channel equalization, and direction finding. This material is available electronically at the companion website. Probability, Random Variables, and Random Processes is the only textbook on probability for engineers that includes relevant background material, provides extensive summaries of key results, and extends various statistical techniques to a range of applications in signal processing. Probability, Random Variables, and Random Processes is a comprehensive textbook on probability theory for engineers that provides a more rigorous mathematical framework than is usually encountered in undergraduate courses. It is intended for first-year graduate students who have some familiarity with probability and random variables, though not necessarily of random processes and systems that operate on random signals. It is also appropriate for advanced undergraduate students who have a strong mathematical background.The book has the following features:Several appendices include related material on integration, important inequalities and identities, frequency-domain transforms, and linear algebra. These topics have been included so that the book is relatively self-contained. One appendix contains an extensive summary of 33 random variables and their properties such as moments, characteristic functions, and entropy.Unlike most books on probability, numerous figures have been included to clarify and expand upon important points. Over 600 illustrations and MATLAB plots have been designed to reinforce the material and illustrate the various characterizations and properties of random quantities.Sufficient statistics are covered in detail, as is their connection to parameter estimation techniques. These include classical Bayesian estimation and several optimality criteria: mean-square error, mean-absolute error, maximum likelihood, method of moments, and least squares.The last four chapters provide an introduction to several topics usually studied in subsequent engineering courses: communication systems and information theory; optimal filtering (Wiener and Kalman); adaptive filtering (FIR and IIR); and antenna beamforming, channel equalization, and direction finding. This material is available electronically at the companion website.Probability, Random Variables, and Random Processes is the only textbook on probability for engineers that includes relevant background material, provides extensive summaries of key results, and extends various statistical techniques to a range of applications in signal processing.Note: The ebook version does not provide access to the companion files. Probability, Random Variables, and Random Processes is a comprehensive textbook on probability theory for engineers that provides a more rigorous mathematical framework than is usually encountered in undergraduate courses. It is intended for first-year graduate students who have some familiarity with probability and random variables, though not necessarily of random processes and systems that operate on random signals. It is also appropriate for advanced undergraduate students who have a strong mathematical background. The book has the following features: * Several appendices include related material on integration, important inequalities and identities, frequency-domain transforms, and linear algebra. These topics have been included so that the book is relatively self-contained. One appendix contains an extensive summary of 33 random variables and their properties such as moments, characteristic functions, and entropy. * Unlike most books on probability, numerous figures have been included to clarify and expand upon important points. Over 600 illustrations and MATLAB plots have been designed to reinforce the material and illustrate the various characterizations and properties of random quantities. * Sufficient statistics are covered in detail, as is their connection to parameter estimation techniques. These include classical Bayesian estimation and several optimality criteria: mean-square error, mean-absolute error, maximum likelihood, method of moments, and least squares. * The last four chapters provide an introduction to several topics usually studied in subsequent engineering courses: communication systems and information theory; optimal filtering (Wiener and Kalman); adaptive filtering (FIR and IIR); and antenna beamforming, channel equalization, and direction finding. This material is available electronically at the companion website. Probability, Random Variables, and Random Processes is the only textbook on probability for engineers that includes relevant background material, provides extensive summaries of key results, and extends various statistical techniques to a range of applications in signal processing. Probability, Random Variables, and Random Processes is a comprehensive textbook on probability theory for engineers that provides a more rigorous mathematical framework than is usually encountered in undergraduate courses. Probability, Random Variables, and Random Processes is a comprehensive textbook on probability theory for engineers that provides a more rigorous mathematical framework than is usually encountered in undergraduate courses. It is intended for first-year graduate students who have some familiarity with probability and random variables, though not necessarily of random processes and systems that operate on random signals. It is also appropriate for advanced undergraduate students who have a strong mathematical background. The book has the following features: Several appendices include related material on integration, important inequalities and identities, frequency-domain transforms, and linear algebra. These topics have been included so that the book is relatively self-contained. One appendix contains an extensive summary of 33 random variables and their properties such as moments, characteristic functions, and entropy. Unlike most books on probability, numerous figures have been included to clarify and expand upon important points. Over 600 illustrations and MATLAB plots have been designed to reinforce the material and illustrate the various characterizations and properties of random quantities. Sufficient statistics are covered in detail, as is their connection to parameter estimation techniques. These include classical Bayesian estimation and several optimality criteria: mean-square error, mean-absolute error, maximum likelihood, method of moments, and least squares. The last four chapters provide an introduction to several topics usually studied in subsequent engineering courses: communication systems and information theory; optimal filtering (Wiener and Kalman); adaptive filtering (FIR and IIR); and antenna beamforming, channel equalization, and direction finding. This material is available electronically at the companion website. Probability, Random Variables, and Random Processes is the only textbook on probability for engineers that includes relevant background material, provides extensive summaries of key results, and extends various statistical techniques to a range of applications in signal processing. |
| Author | Shynk, John J |
| Author_xml | – sequence: 1 fullname: Shynk, John J |
| BackLink | https://cir.nii.ac.jp/crid/1130282269578660480$$DView record in CiNii |
| BookMark | eNqFkE9vEzEQxbeCVpC236CHVEIgpEaa8X8faRSgUiR6QCBOlr3rTd2467DeBMqnxzSh6a0-2OPxT2_83qh62aXOH1QjRFRUU834i6eXw2pEAAmAQiWOygtlEojkSr-qTnO-hbIkJ5qI1xW97pOzLsQw3F-Me9s16W68sX2wLvp8MS6N_91Vn2qfs88n1WFrY_anu_O4-vZx9nX6eTL_8ulq-mE-sRwkkgkToLQSLW0oE62ygG0tBdeuVghENcAtWOpqLQXQVmpLVC0cqXljoXXY0OPq7VY4L0OMObWDcSktM2G_pXFLX3xwJFQW8P0WtHnpf-WbFIdsNtE_0EZL9RiOLuybnahti8-totmQpxgX-9nF9s-1z4N5UKt9N_Q2mtnlVGnGCCvgu2dAiUCLw0Ke7UjfR79Iu9EIiKD5_mNdCKYO_3ZEWoIiRGgulRDAFBTsfIvd2X79x3dm1YdS3hta4kBKCr3PIyxWaxdDvgnd4hH8fjWf_bgssOBK0L-gsKfN |
| ContentType | eBook Book |
| DBID | WIIVT MOSFZ PS5 RYH YSPEL OHILO OODEK |
| DEWEY | 620.0015192 |
| DatabaseName | Wiley Maruzen eBook Library Maruzen eBook Library (Global) CiNii Complete Perlego O'Reilly Online Learning: Corporate Edition O'Reilly Online Learning: Academic/Public Library Edition |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Applied Sciences Statistics Mathematics Engineering |
| DocumentTitleAlternate | Probability, random variables, and random processes |
| EISBN | 1118393945 9781118393949 0470242094 9780470242094 |
| Edition | 1st ed. 1 |
| ExternalDocumentID | bke00051237 9781118393949 9781118393956 EBC894424 EBC7103603 1011095 BB11136843 3000132226 WILEYB0016586 |
| Genre | Electronic books Textbooks |
| GroupedDBID | 089 20A 5VX A4J AABBV AAJKE ABARN ABBFG ABIAV ABQPQ ACBYE ACCPI ACGYG ACLGV ACNUM ADVEM AERYV AFOJC AHWGJ AJFER AKHYG AKQZE ALMA_UNASSIGNED_HOLDINGS AMYDA AZZ BBABE BC- CZZ GEOUK HF4 IWG J-X JJU MOSFZ MYL OHILO OODEK PQQKQ UZ6 WCYEB WIIVT WLZGU WZG YSPEL PS5 RYH ALTAS ASGYQ IVK LMJTD PLCCB PQEST PQUKI P~0 V1R |
| ID | FETCH-LOGICAL-a50712-4608986f3d346f8a01fc7659bc81028d05a0a3bc97603f79a28c6b2c5da0fb1d3 |
| ISBN | 1118393945 9781118393949 0470242094 9780470242094 9781118393956 1118393953 |
| IngestDate | Fri Sep 23 13:45:17 EDT 2022 Mon Sep 15 23:49:41 EDT 2025 Fri Dec 05 22:00:27 EST 2025 Sun Nov 09 05:52:42 EST 2025 Wed Nov 26 03:15:37 EST 2025 Tue Dec 02 19:02:45 EST 2025 Fri Jun 27 00:17:01 EDT 2025 Tue Nov 18 22:28:18 EST 2025 Tue Oct 07 10:28:04 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | false |
| LCCN | 2012008186 |
| LCCallNum | QA274.S53 2012 |
| LCCallNum_Ident | QA274.S53 2012 |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-a50712-4608986f3d346f8a01fc7659bc81028d05a0a3bc97603f79a28c6b2c5da0fb1d3 |
| Notes | Includes bibliographical reference (p. 743-753) and index Available also in a print ed. Mode of access: Internet via World Wide Web. Title from title screen. |
| OCLC | 1347027589 818854237 |
| PQID | EBC7103603 |
| PageCount | 796 |
| ParticipantIDs | skillsoft_books24x7_bke00051237 askewsholts_vlebooks_9781118393949 safari_books_v2_9781118393956 proquest_ebookcentral_EBC894424 proquest_ebookcentral_EBC7103603 perlego_books_1011095 nii_cinii_1130282269578660480 maruzen_primary_3000132226 igpublishing_primary_WILEYB0016586 |
| ProviderPackageCode | J-X |
| PublicationCentury | 2000 |
| PublicationDate | 2012. 2013/01/01 c2013 2012 2012-11-06T00:00:00 2012-09-17 |
| PublicationDateYYYYMMDD | 2012-01-01 2013-01-01 2012-11-06 2012-09-17 |
| PublicationDate_xml | – year: 2012 text: 2012 |
| PublicationDecade | 2010 |
| PublicationPlace | Hoboken, NJ |
| PublicationPlace_xml | – name: Hoboken, NJ – name: Hoboken, N.J – name: Newark – name: Somerset |
| PublicationYear | 2012 2013 |
| Publisher | Wiley John Wiley & Sons, Incorporated Wiley-Interscience Wiley-Blackwell John Wiley & Sons |
| Publisher_xml | – name: Wiley – name: John Wiley & Sons, Incorporated – name: Wiley-Interscience – name: Wiley-Blackwell – name: John Wiley & Sons |
| SSID | ssj0000752926 |
| Score | 1.8838003 |
| Snippet | Probability, Random Variables, and Random Processes is a comprehensive textbook on probability theory for engineers that provides a more rigorous mathematical... |
| SourceID | skillsoft askewsholts safari proquest perlego nii maruzen igpublishing |
| SourceType | Aggregation Database Publisher |
| SubjectTerms | Algorithms COMPUTERS Engineering Engineering-Statistical methods-Textbooks Probabilities Probabilities-Textbooks Programming Statistical methods Stochastic processes Stochastic processes-Textbooks Textbooks |
| SubjectTermsDisplay | Algorithms COMPUTERS Electronic books. Engineering–Statistical methods–Textbooks Probabilities–Textbooks Programming Stochastic processes–Textbooks |
| Subtitle | theory and signal processing applications |
| TableOfContents | 3.8.11 Gamma Random Variable (Erlang for r ∈ N) -- 3.8.12 Beta Random Variable (Arcsine for α = β = 1/2, Power Function for β = 1) -- 3.8.13 Pareto Random Variable -- 3.8.14 Weibull Random Variable -- 3.8.15 Logistic Random Variable (Sigmoid for {μ = 0, α = 1}) -- 3.8.16 Chi Random Variable (Maxwell-Boltzmann, Half-Normal) -- 3.8.17 Chi-Square Random Variable -- 3.8.18 F-Distribution -- 3.8.19 Student's t Distribution -- 3.8.20 Extreme Value Distribution (Type I: Gumbel) -- 3.9 Discrete Random Variables -- 3.9.1 Bernoulli Random Variable -- 3.9.2 Binomial Random Variable -- 3.9.3 Geometric Random Variable (with Support Z+ or N) -- 3.9.4 Negative Binomial Random Variable (Pascal) -- 3.9.5 Poisson Random Variable -- 3.9.6 Hypergeometric Random Variable -- 3.9.7 Discrete Uniform Random Variable -- 3.9.8 Logarithmic Random Variable (Log-Series) -- 3.9.9 Zeta Random Variable (Zipf) -- Problems -- Further Reading -- 4 Multiple Random Variables -- 4.1 Introduction -- 4.2 Random Variable Approximations -- 4.2.1 Binomial Approximation of Hypergeometric -- 4.2.2 Poisson Approximation of Binomial -- 4.2.3 Gaussian Approximations -- 4.2.4 Gaussian Approximation of Binomial -- 4.2.5 Gaussian Approximation of Poisson -- 4.2.6 Gaussian Approximation of Hypergeometric -- 4.3 Joint and Marginal Distributions -- 4.4 Independent Random Variables -- 4.5 Conditional Distribution -- 4.6 Random Vectors -- 4.6.1 Bivariate Uniform Distribution -- 4.6.2 Multivariate Gaussian Distribution -- 4.6.3 Multivariate Student's t Distribution -- 4.6.4 Multinomial Distribution -- 4.6.5 Multivariate Hypergeometric Distribution -- 4.6.6 Bivariate Exponential Distributions -- 4.7 Generating Dependent Random Variables -- 4.8 Random Variable Transformations -- 4.8.1 Transformations of Discrete Random Variables -- 4.8.2 Transformations of Continuous Random Variables Intro -- PROBABILITY, RANDOM VARIABLES, AND RANDOM PROCESSES -- CONTENTS -- PREFACE -- NOTATION -- 1 Overview and Background -- 1.1 Introduction -- 1.1.1 Signals, Signal Processing, and Communications -- 1.1.2 Probability, Random Variables, and Random Vectors -- 1.1.3 Random Sequences and Random Processes -- 1.1.4 Delta Functions -- 1.2 Deterministic Signals and Systems -- 1.2.1 Continuous Time -- 1.2.2 Discrete Time -- 1.2.3 Discrete-Time Filters -- 1.2.4 State-Space Realizations -- 1.3 Statistical Signal Processing with MATLAB® -- 1.3.1 Random Number Generation -- 1.3.2 Filtering -- Problems -- Further Reading -- PART I Probability, Random Variables, and Expectation -- 2 Probability Theory -- 2.1 Introduction -- 2.2 Sets and Sample Spaces -- 2.3 Set Operations -- 2.4 Events and Fields -- 2.5 Summary of a Random Experiment -- 2.6 Measure Theory -- 2.7 Axioms of Probability -- 2.8 Basic Probability Results -- 2.9 Conditional Probability -- 2.10 Independence -- 2.11 Bayes' Formula -- 2.12 Total Probability -- 2.13 Discrete Sample Spaces -- 2.14 Continuous Sample Spaces -- 2.15 Nonmeasurable Subsets of R -- Problems -- Further Reading -- 3 Random Variables -- 3.1 Introduction -- 3.2 Functions and Mappings -- 3.3 Distribution Function -- 3.4 Probability Mass Function -- 3.5 Probability Density Function -- 3.6 Mixed Distributions -- 3.7 Parametric Models for Random Variables -- 3.8 Continuous Random Variables -- 3.8.1 Gaussian Random Variable (Normal) -- 3.8.2 Log-Normal Random Variable -- 3.8.3 Inverse Gaussian Random Variable (Wald) -- 3.8.4 Exponential Random Variable (One-Sided) -- 3.8.5 Laplace Random Variable (Double-Sided Exponential) -- 3.8.6 Cauchy Random Variable -- 3.8.7 Continuous Uniform Random Variable -- 3.8.8 Triangular Random Variable -- 3.8.9 Rayleigh Random Variable -- 3.8.10 Rice Random Variable 6.9 Markov Sequence -- 6.10 Markov Process -- 6.11 Random Sequences -- 6.11.1 Bernoulli Sequence -- 6.11.2 Bernoulli Scheme -- 6.11.3 Independent Sequences -- 6.11.4 Bernoulli Random Walk -- 6.11.5 Binomial Counting Sequence -- 6.12 Random Processes -- 6.12.1 Poisson Counting Process -- 6.12.2 Random Telegraph Signal -- 6.12.3 Wiener Process -- 6.12.4 Gaussian Process -- 6.12.5 Pulse Amplitude Modulation -- 6.12.6 Random Sine Signals -- Problems -- Further Reading -- 7 Stochastic Convergence, Calculus, and Decompositions -- 7.1 Introduction -- 7.2 Stochastic Convergence -- 7.3 Laws of Large Numbers -- 7.4 Central Limit Theorem -- 7.5 Stochastic Continuity -- 7.6 Derivatives and Integrals -- 7.7 Differential Equations -- 7.8 Difference Equations -- 7.9 Innovations and Mean-Square Predictability -- 7.10 Doob-Meyer Decomposition -- 7.11 Karhunen-Lo`eve Expansion -- Problems -- Further Reading -- 8 Systems, Noise, and Spectrum Estimation -- 8.1 Introduction -- 8.2 Correlation Revisited -- 8.3 Ergodicity -- 8.4 Eigenfunctions of RXX(τ) -- 8.5 Power Spectral Density -- 8.6 Power Spectral Distribution -- 8.7 Cross-Power Spectral Density -- 8.8 Systems with Random Inputs -- 8.8.1 Nonlinear Systems -- 8.8.2 Linear Systems -- 8.9 Passband Signals -- 8.10 White Noise -- 8.11 Bandwidth -- 8.12 Spectrum Estimation -- 8.12.1 Periodogram -- 8.12.2 Smoothed Periodogram -- 8.12.3 Modified Periodogram -- 8.13 Parametric Models -- 8.13.1 Autoregressive Model -- 8.13.2 Moving-Average Model -- 8.13.3 Autoregressive Moving-Average Model -- 8.14 System Identification -- Problems -- Further Reading -- 9 Sufficient Statistics and Parameter Estimation -- 9.1 Introduction -- 9.2 Statistics -- 9.3 Sufficient Statistics -- 9.4 Minimal Sufficient Statistic -- 9.5 Exponential Families -- 9.6 Location-Scale Families -- 9.7 Complete Statistic -- 9.8 Rao-Blackwell Theorem A.4.6 Hypergeometric 4.9 Important Functions of Two Random Variables -- 4.9.1 Sum: Z = X + Y -- 4.9.2 Difference: Z = X - Y -- 4.9.3 Product: Z = XY -- 4.9.4 Quotient (Ratio): Z = X/Y -- 4.10 Transformations of Random Variable Families -- 4.10.1 Gaussian Transformations -- 4.10.2 Exponential Transformations -- 4.10.3 Chi-Square Transformations -- 4.11 Transformations of Random Vectors -- 4.12 Sample Mean and Sample Variance S2 -- 4.13 Minimum, Maximum, and Order Statistics -- 4.14 Mixtures -- Problems -- Further Reading -- 5 Expectation and Moments -- 5.1 Introduction -- 5.2 Expectation and Integration -- 5.3 Indicator Random Variable -- 5.4 Simple Random Variable -- 5.5 Expectation for Discrete Sample Spaces -- 5.6 Expectation for Continuous Sample Spaces -- 5.7 Summary of Expectation -- 5.8 Functional View of the Mean -- 5.9 Properties of Expectation -- 5.10 Expectation of a Function -- 5.11 Characteristic Function -- 5.12 Conditional Expectation -- 5.13 Properties of Conditional Expectation -- 5.14 Location Parameters: Mean, Median, and Mode -- 5.15 Variance, Covariance, and Correlation -- 5.16 Functional View of the Variance -- 5.17 Expectation and the Indicator Function -- 5.18 Correlation Coefficients -- 5.19 Orthogonality -- 5.20 Correlation and Covariance Matrices -- 5.21 Higher Order Moments and Cumulants -- 5.22 Functional View of Skewness -- 5.23 Functional View of Kurtosis -- 5.24 Generating Functions -- 5.25 Fourth-Order Gaussian Moment -- 5.26 Expectations of Nonlinear Transformations -- Problems -- Further Reading -- PART II Random Processes, Systems, and Parameter Estimation -- 6 Random Processes -- 6.1 Introduction -- 6.2 Characterizations of a Random Process -- 6.3 Consistency and Extension -- 6.4 Types of Random Processes -- 6.5 Stationarity -- 6.6 Independent and Identically Distributed -- 6.7 Independent Increments -- 6.8 Martingales 9.9 Lehmann-SchefféTheorem -- 9.10 Bayes Estimation -- 9.11 Mean-Square-Error Estimation -- 9.12 Mean-Absolute-Error Estimation -- 9.13 Orthogonality Condition -- 9.14 Properties of Estimators -- 9.14.1 Unbiased -- 9.14.2 Consistent -- 9.14.3 Efficient -- 9.15 Maximum A Posteriori Estimation -- 9.16 Maximum Likelihood Estimation -- 9.17 Likelihood Ratio Test -- 9.18 Expectation-Maximization Algorithm -- 9.19 Method of Moments -- 9.20 Least-Squares Estimation -- 9.21 Properties of LS Estimators -- 9.21.1 Minimum ξWLS -- 9.21.2 Uniqueness -- 9.21.3 Orthogonality -- 9.21.4 Unbiased -- 9.21.5 Covariance Matrix -- 9.21.6 Efficient: Achieves CRLB -- 9.21.7 BLU Estimator -- 9.22 Best Linear Unbiased Estimation -- 9.23 Properties of BLU Estimators -- Problems -- Further Reading -- A Note on Part III of the Book -- APPENDICES Introduction to Appendices -- A Summaries of Univariate Parametric Distributions -- A.1 Notation -- A.2 Further Reading -- A.3 Continuous Random Variables -- A.3.1 Beta (Arcsine for α = β = 1/2, Power Function for β = 1) -- A.3.2 Cauchy -- A.3.3 Chi -- A.3.4 Chi-Square -- A.3.5 Exponential (Shifted by c) -- A.3.6 Extreme Value (Type I: Gumbel) -- A.3.7 F-Distribution -- A.3.8 Gamma (Erlang for r ∈ N with Γ (r ) = (r - 1)!) -- A.3.9 Gaussian (Normal) -- A.3.10 Half-Normal (Folded Normal) -- A.3.11 Inverse Gaussian (Wald) -- A.3.12 Laplace (Double-Sided Exponential) -- A.3.13 Logistic (Sigmoid for {μ = 0, α = 1}) -- A.3.14 Log-Normal -- A.3.15 Maxwell-Boltzmann -- A.3.16 Pareto -- A.3.17 Rayleigh -- A.3.18 Rice -- A.3.19 Student's t Distribution -- A.3.20 Triangular -- A.3.21 Uniform (Continuous) -- A.3.22 Weibull -- A.4 Discrete Random Variables -- A.4.1 Bernoulli (with Support {0, 1}) -- A.4.2 Bernoulli (Symmetric with Support {-1, 1}) -- A.4.3 Binomial -- A.4.4 Geometric (with Support Z+) -- A.4.5 Geometric (Shifted with Support N) |
| Title | Probability, random variables, and random processes |
| URI | http://portal.igpublish.com/iglibrary/search/WILEYB0016586.html https://elib.maruzen.co.jp/elib/html/BookDetail/Id/3000132226 https://cir.nii.ac.jp/crid/1130282269578660480 https://www.perlego.com/book/1011095/probability-random-variables-and-random-processes-theory-and-signal-processing-applications-pdf https://ebookcentral.proquest.com/lib/[SITE_ID]/detail.action?docID=7103603 https://ebookcentral.proquest.com/lib/[SITE_ID]/detail.action?docID=894424 https://learning.oreilly.com/library/view/~/9781118393956/?ar https://www.vlebooks.com/vleweb/product/openreader?id=none&isbn=9781118393949&uid=none http://www.books24x7.com/marc.asp?bookid=51237 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3Nb9MwFLdYQYKe-Biig7EIcduCnNhxbI6dCoiPscNAu0VO4nRRS1o1XVX463nPTrJ0EkIcuFiJbTmOf7bfz8_2e4S85owaIzX1MxAWPg946qcKADFZyKC_KCq09VryOT47k5eX6rzxQFhbdwJxVcntVi3_K9QQB2Dj1dl_gLsrFCLgGUCHEGCH8BYj7l4d4ucrGJv2rKttOBBC-eLH8QYWw3g9qm7PaTbxS3dDALWu7mgHbrZbLXo5RYbapNs7jL1N7k4jc_WzmrVneY8_vulrD9CTw472wE49O0tKymMU29S5Hr5li3o8Rs_0QnK2R_ZgoTIgd99Pvn771Gm1gICEKhT26n9bTmvmqH0fkqGuZzCFw_S-rtFE7HTZqdzQj5NeXf8yFQj7qizhfWlWczNd7C4Bal1A8w3J_XpWzuc1iKweLbh4SAZ4VeQRuWOqx2T4pbOAWz8hSQ-NE8-1uddhceJBRBvbIeG99RwONtXh4N3g4PVx2Cff300uTj_4jTMLXyPlDn0uqFRSFCxnXBQwPIIii0Wk0kwiyctppKlmaQb8kLIiVjqUmUjDLMo1LdIgZ0_JoFpU5hnxgKXr1OQy0kXOAxMpmlNlqZ9O0Tz_iLzqtXCymduN9zpB-2YBUmKmuIJM_YZPls66SWJtuY9xKRBJKOmggaNLZ7TZn4PEQ0AoyUoMA9wBB7YpFMgAIdBUwYjsN9gl7vsBkk0VjYjXIpnYmjXnkJPJ-BQIL4P_H5GjP2aRivOQw8ddH2jK3oT934ugckdd13BZQr6Nk3Rm7LIlZPHBX6r_nDy4GTEvyGC9ujaH5F62WZf16mXT838Dz6mBeg |
| linkProvider | ProQuest Ebooks |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.title=Probability%2C+random+variables%2C+and+random+processes+%3A+theory+and+signal+processing+applications&rft.au=Shynk%2C+John+J.&rft.date=2013-01-01&rft.pub=Wiley&rft.isbn=9780470242094&rft.externalDocID=BB11136843 |
| thumbnail_l | http://cvtisr.summon.serialssolutions.com/2.0.0/image/custom?url=https%3A%2F%2Fwww.perlego.com%2Fbooks%2FRM_Books%2Fwiley_hlvwyirv%2F9781118393956.jpg |
| thumbnail_m | http://cvtisr.summon.serialssolutions.com/2.0.0/image/custom?url=https%3A%2F%2Fwww.safaribooksonline.com%2Flibrary%2Fcover%2F9781118393956 http://cvtisr.summon.serialssolutions.com/2.0.0/image/custom?url=https%3A%2F%2Fvle.dmmserver.com%2Fmedia%2F640%2F97811183%2F9781118393949.jpg |
| thumbnail_s | http://cvtisr.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fportal.igpublish.com%2Figlibrary%2Famazonbuffer%2FWILEYB0016586_null_0_320.png |