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Biblioteca

MARC Lista (tabellare)
Mathematical foundations for linear circuits and systems in engineering / / John J. Shynk
Mathematical foundations for linear circuits and systems in engineering / / John J. Shynk
Autore Shynk John Joseph
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , 2016
Descrizione fisica 1 online resource (771 pages) : illustrations, tables
Disciplina 621.3815
Soggetto topico Electric circuits - Mathematical models
Electric circuits, Linear
Electric circuit analysis
ISBN 1-119-07339-1
1-119-07344-8
1-119-07340-5
Classificazione 541.2
621.3815
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910795942003321
Shynk John Joseph  
Hoboken, New Jersey : , : Wiley, , 2016
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Mathematical foundations for linear circuits and systems in engineering / / John J. Shynk
Mathematical foundations for linear circuits and systems in engineering / / John J. Shynk
Autore Shynk John Joseph
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , 2016
Descrizione fisica 1 online resource (771 pages) : illustrations, tables
Disciplina 621.3815
Soggetto topico Electric circuits - Mathematical models
Electric circuits, Linear
Electric circuit analysis
ISBN 1-119-07339-1
1-119-07344-8
1-119-07340-5
Classificazione 541.2
621.3815
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910814571703321
Shynk John Joseph  
Hoboken, New Jersey : , : Wiley, , 2016
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Probability, random variables, and random processes [[electronic resource] ] : theory and signal processing applications / / John J. Shynk
Probability, random variables, and random processes [[electronic resource] ] : theory and signal processing applications / / John J. Shynk
Autore Shynk John Joseph
Pubbl/distr/stampa Hoboken, NJ, : Wiley, 2012, c2013
Descrizione fisica 1 online resource (796 p.)
Disciplina 519.2
Soggetto topico Probabilities
Stochastic processes
Engineering - Statistical methods
Soggetto genere / forma Electronic books.
ISBN 1-283-64495-9
1-118-39394-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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 Expectation2 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 Function3.5 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
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
Record Nr. UNINA-9910462363403321
Shynk John Joseph  
Hoboken, NJ, : Wiley, 2012, c2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Probability, random variables, and random processes [[electronic resource] ] : theory and signal processing applications / / John J. Shynk
Probability, random variables, and random processes [[electronic resource] ] : theory and signal processing applications / / John J. Shynk
Autore Shynk John Joseph
Pubbl/distr/stampa Hoboken, NJ, : Wiley, 2012, c2013
Descrizione fisica xxvi, 768 p. : ill
Disciplina 519.2
Soggetto topico Engineering - Statistical methods
Probabilities
Stochastic processes
ISBN 1-283-64495-9
1-118-39394-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910795825703321
Shynk John Joseph  
Hoboken, NJ, : Wiley, 2012, c2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Probability, random variables, and random processes [[electronic resource] ] : theory and signal processing applications / / John J. Shynk
Probability, random variables, and random processes [[electronic resource] ] : theory and signal processing applications / / John J. Shynk
Autore Shynk John Joseph
Edizione [1st ed.]
Pubbl/distr/stampa Hoboken, NJ, : Wiley, 2012, c2013
Descrizione fisica xxvi, 768 p. : ill
Disciplina 519.2
Soggetto topico Engineering - Statistical methods
Probabilities
Stochastic processes
ISBN 1-283-64495-9
1-118-39394-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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.
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.
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.
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.
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).
A.4.6 Hypergeometric.
Record Nr. UNINA-9910814681703321
Shynk John Joseph  
Hoboken, NJ, : Wiley, 2012, c2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui