top

  Info

  • Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.

  Info

  • Utilizzare questo link per rimuovere la selezione effettuata.

Biblioteca

MARC Lista (tabellare)
Environmental data analysis with MatLab [[electronic resource] /] / William Menke, Joshua Menke
Environmental data analysis with MatLab [[electronic resource] /] / William Menke, Joshua Menke
Autore Menke William
Edizione [1st ed.]
Pubbl/distr/stampa Amsterdam ; ; Boston, : Elsevier, c2012
Descrizione fisica 1 online resource (282 p.)
Disciplina 363.7001/5118
Altri autori (Persone) MenkeJoshua E <1976-> (Joshua Ephraim)
Soggetto topico Environmental sciences - Mathematical models
Environmental sciences - Data processing
Soggetto genere / forma Electronic books.
ISBN 1-283-24992-8
9786613249920
0-12-391887-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Front Cover; Environmental Data Analysis with MatLab; Copyright; Dedication; Preface; Advice on scripting for beginners; Contents; Chapter 1: Data analysis with MatLab; 1.1. Why MatLab?; 1.2. Getting started with MatLab; 1.3. Getting organized; 1.4. Navigating folders; 1.5. Simple arithmetic and algebra; 1.6. Vectors and matrices; 1.7. Multiplication of vectors of matrices; 1.8. Element access; 1.9. To loop or not to loop; 1.10. The matrix inverse; 1.11. Loading data from a file; 1.12. Plotting data; 1.13. Saving data to a file; 1.14. Some advice on writing scripts; Problems
Chapter 2: A first look at data2.1. Look at your data!; 2.2. More on MatLab graphics; 2.3. Rate information; 2.4. Scatter plots and their limitations; Problems; Chapter 3: Probability and what it has to do with data analysis; 3.1. Random variables; 3.2. Mean, median, and mode; 3.3. Variance; 3.4. Two important probability density functions; 3.5. Functions of a random variable; 3.6. Joint probabilities; 3.7. Bayesian inference; 3.8. Joint probability density functions; 3.9. Covariance; 3.10. Multivariate distributions; 3.11. The multivariate Normal distributions
3.12. Linear functions of multivariate dataProblems; Chapter 4: The power of linear models; 4.1. Quantitative models, data, and model parameters; 4.2. The simplest of quantitative models; 4.3. Curve fitting; 4.4. Mixtures; 4.5. Weighted averages; 4.6. Examining error; 4.7. Least squares; 4.8. Examples; 4.9. Covariance and the behavior of error; Problems; Chapter 5: Quantifying preconceptions; 5.1. When least square fails; 5.2. Prior information; 5.3. Bayesian inference; 5.4. The product of Normal probability density distributions; 5.5. Generalized least squares
5.6. The role of the covariance of the data5.7. Smoothness as prior information; 5.8. Sparse matrices; 5.9. Reorganizing grids of model parameters; Problems; Chapter 6: Detecting periodicities; 6.1. Describing sinusoidal oscillations; 6.2. Models composed only of sinusoidal functions; 6.3. Going complex; 6.4. Lessons learned from the integral transform; 6.5. Normal curve; 6.6. Spikes; 6.7. Area under a function; 6.8. Time-delayed function; 6.9. Derivative of a function; 6.10. Integral of a function; 6.11. Convolution; 6.12. Nontransient signals; Problems
Chapter 7: The past influences the present7.1. Behavior sensitive to past conditions; 7.2. Filtering as convolution; 7.3. Solving problems with filters; 7.4. Predicting the future; 7.5. A parallel between filters and polynomials; 7.6. Filter cascades and inverse filters; 7.7. Making use of what you know; Problems; Chapter 8: Patterns suggested by data; 8.1. Samples as mixtures; 8.2. Determining the minimum number of factors; 8.3. Application to the Atlantic Rocks dataset; 8.4. Spiky factors; 8.5. Time-Variable functions; Problems; Chapter 9: Detecting correlations among data
9.1. Correlation is covariance
Record Nr. UNINA-9910456680203321
Menke William  
Amsterdam ; ; Boston, : Elsevier, c2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Environmental data analysis with MatLab [[electronic resource] /] / William Menke, Joshua Menke
Environmental data analysis with MatLab [[electronic resource] /] / William Menke, Joshua Menke
Autore Menke William
Edizione [1st ed.]
Pubbl/distr/stampa Amsterdam ; ; Boston, : Elsevier, c2012
Descrizione fisica 1 online resource (282 p.)
Disciplina 363.7001/5118
Altri autori (Persone) MenkeJoshua E <1976-> (Joshua Ephraim)
Soggetto topico Environmental sciences - Mathematical models
Environmental sciences - Data processing
ISBN 1-283-24992-8
9786613249920
0-12-391887-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Front Cover; Environmental Data Analysis with MatLab; Copyright; Dedication; Preface; Advice on scripting for beginners; Contents; Chapter 1: Data analysis with MatLab; 1.1. Why MatLab?; 1.2. Getting started with MatLab; 1.3. Getting organized; 1.4. Navigating folders; 1.5. Simple arithmetic and algebra; 1.6. Vectors and matrices; 1.7. Multiplication of vectors of matrices; 1.8. Element access; 1.9. To loop or not to loop; 1.10. The matrix inverse; 1.11. Loading data from a file; 1.12. Plotting data; 1.13. Saving data to a file; 1.14. Some advice on writing scripts; Problems
Chapter 2: A first look at data2.1. Look at your data!; 2.2. More on MatLab graphics; 2.3. Rate information; 2.4. Scatter plots and their limitations; Problems; Chapter 3: Probability and what it has to do with data analysis; 3.1. Random variables; 3.2. Mean, median, and mode; 3.3. Variance; 3.4. Two important probability density functions; 3.5. Functions of a random variable; 3.6. Joint probabilities; 3.7. Bayesian inference; 3.8. Joint probability density functions; 3.9. Covariance; 3.10. Multivariate distributions; 3.11. The multivariate Normal distributions
3.12. Linear functions of multivariate dataProblems; Chapter 4: The power of linear models; 4.1. Quantitative models, data, and model parameters; 4.2. The simplest of quantitative models; 4.3. Curve fitting; 4.4. Mixtures; 4.5. Weighted averages; 4.6. Examining error; 4.7. Least squares; 4.8. Examples; 4.9. Covariance and the behavior of error; Problems; Chapter 5: Quantifying preconceptions; 5.1. When least square fails; 5.2. Prior information; 5.3. Bayesian inference; 5.4. The product of Normal probability density distributions; 5.5. Generalized least squares
5.6. The role of the covariance of the data5.7. Smoothness as prior information; 5.8. Sparse matrices; 5.9. Reorganizing grids of model parameters; Problems; Chapter 6: Detecting periodicities; 6.1. Describing sinusoidal oscillations; 6.2. Models composed only of sinusoidal functions; 6.3. Going complex; 6.4. Lessons learned from the integral transform; 6.5. Normal curve; 6.6. Spikes; 6.7. Area under a function; 6.8. Time-delayed function; 6.9. Derivative of a function; 6.10. Integral of a function; 6.11. Convolution; 6.12. Nontransient signals; Problems
Chapter 7: The past influences the present7.1. Behavior sensitive to past conditions; 7.2. Filtering as convolution; 7.3. Solving problems with filters; 7.4. Predicting the future; 7.5. A parallel between filters and polynomials; 7.6. Filter cascades and inverse filters; 7.7. Making use of what you know; Problems; Chapter 8: Patterns suggested by data; 8.1. Samples as mixtures; 8.2. Determining the minimum number of factors; 8.3. Application to the Atlantic Rocks dataset; 8.4. Spiky factors; 8.5. Time-Variable functions; Problems; Chapter 9: Detecting correlations among data
9.1. Correlation is covariance
Record Nr. UNINA-9910781791903321
Menke William  
Amsterdam ; ; Boston, : Elsevier, c2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Environmental data analysis with MatLab / / William Menke, Joshua Menke
Environmental data analysis with MatLab / / William Menke, Joshua Menke
Autore Menke William
Edizione [1st ed.]
Pubbl/distr/stampa Amsterdam ; ; Boston, : Elsevier, c2012
Descrizione fisica 1 online resource (282 p.)
Disciplina 363.7001/5118
363.70015118
Altri autori (Persone) MenkeJoshua E <1976-> (Joshua Ephraim)
Soggetto topico Environmental sciences - Mathematical models
Environmental sciences - Data processing
ISBN 9786613249920
9781283249928
1283249928
9780123918871
0123918871
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Front Cover; Environmental Data Analysis with MatLab; Copyright; Dedication; Preface; Advice on scripting for beginners; Contents; Chapter 1: Data analysis with MatLab; 1.1. Why MatLab?; 1.2. Getting started with MatLab; 1.3. Getting organized; 1.4. Navigating folders; 1.5. Simple arithmetic and algebra; 1.6. Vectors and matrices; 1.7. Multiplication of vectors of matrices; 1.8. Element access; 1.9. To loop or not to loop; 1.10. The matrix inverse; 1.11. Loading data from a file; 1.12. Plotting data; 1.13. Saving data to a file; 1.14. Some advice on writing scripts; Problems
Chapter 2: A first look at data2.1. Look at your data!; 2.2. More on MatLab graphics; 2.3. Rate information; 2.4. Scatter plots and their limitations; Problems; Chapter 3: Probability and what it has to do with data analysis; 3.1. Random variables; 3.2. Mean, median, and mode; 3.3. Variance; 3.4. Two important probability density functions; 3.5. Functions of a random variable; 3.6. Joint probabilities; 3.7. Bayesian inference; 3.8. Joint probability density functions; 3.9. Covariance; 3.10. Multivariate distributions; 3.11. The multivariate Normal distributions
3.12. Linear functions of multivariate dataProblems; Chapter 4: The power of linear models; 4.1. Quantitative models, data, and model parameters; 4.2. The simplest of quantitative models; 4.3. Curve fitting; 4.4. Mixtures; 4.5. Weighted averages; 4.6. Examining error; 4.7. Least squares; 4.8. Examples; 4.9. Covariance and the behavior of error; Problems; Chapter 5: Quantifying preconceptions; 5.1. When least square fails; 5.2. Prior information; 5.3. Bayesian inference; 5.4. The product of Normal probability density distributions; 5.5. Generalized least squares
5.6. The role of the covariance of the data5.7. Smoothness as prior information; 5.8. Sparse matrices; 5.9. Reorganizing grids of model parameters; Problems; Chapter 6: Detecting periodicities; 6.1. Describing sinusoidal oscillations; 6.2. Models composed only of sinusoidal functions; 6.3. Going complex; 6.4. Lessons learned from the integral transform; 6.5. Normal curve; 6.6. Spikes; 6.7. Area under a function; 6.8. Time-delayed function; 6.9. Derivative of a function; 6.10. Integral of a function; 6.11. Convolution; 6.12. Nontransient signals; Problems
Chapter 7: The past influences the present7.1. Behavior sensitive to past conditions; 7.2. Filtering as convolution; 7.3. Solving problems with filters; 7.4. Predicting the future; 7.5. A parallel between filters and polynomials; 7.6. Filter cascades and inverse filters; 7.7. Making use of what you know; Problems; Chapter 8: Patterns suggested by data; 8.1. Samples as mixtures; 8.2. Determining the minimum number of factors; 8.3. Application to the Atlantic Rocks dataset; 8.4. Spiky factors; 8.5. Time-Variable functions; Problems; Chapter 9: Detecting correlations among data
9.1. Correlation is covariance
Record Nr. UNINA-9910826804703321
Menke William  
Amsterdam ; ; Boston, : Elsevier, c2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Geophysical data analysis : discrete inverse theory / / William Menke
Geophysical data analysis : discrete inverse theory / / William Menke
Autore Menke William
Pubbl/distr/stampa London, United Kingdom : , : Academic Press, an imprint of Elsevier, , [2018]
Descrizione fisica 1 online resource (539 pages)
Disciplina 551.01519
Soggetto topico Geophysics - Measurement
Oceanography - Measurement
Inverse problems (Differential equations) - Numerical solutions
ISBN 0-12-813556-5
0-12-813555-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Describing inverse problems -- Some comments on probability theory -- Solution of the linear, Gaussian inverse problem, viewpoint 1 : the length method -- Solution of the linear, Gaussian inverse problem, viewpoint 2 : generalized inverses -- Solution of the linear, Gaussian inverse problem, viewpoint 3 : maximum likelihood methods -- Nonuniqueness and localized averages -- Applications of vector spaces -- Linear inverse problems and non-Gaussian statistics -- Nonlinear inverse problems -- Factor analysis -- Continuous inverse theory and tomography -- Sample inverse problems -- Applications of inverse theory to solid earth geophysics.
Record Nr. UNINA-9910583476103321
Menke William  
London, United Kingdom : , : Academic Press, an imprint of Elsevier, , [2018]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Geophysical data analysis [[electronic resource] ] : discrete inverse theory / / William Menke
Geophysical data analysis [[electronic resource] ] : discrete inverse theory / / William Menke
Autore Menke William
Edizione [Matlab ed., 3rd ed.]
Pubbl/distr/stampa Amsterdam ; ; Boston, : Elsevier/AP, 2012
Descrizione fisica 1 online resource (331 p.)
Disciplina 551
Soggetto topico Geophysics - Measurement
Inverse problems (Differential equations) - Numerical solutions
Oceanography - Measurement
Soggetto genere / forma Electronic books.
ISBN 1-283-73512-1
0-12-397784-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Front Cover; Geophysical Data Analysis: Discrete Inverse Theory; Copyright; Dedication; Preface; Reference; Companion Web Site; Contents; Introduction; I.1. Forward and Inverse Theories; I.2. MatLab as a Tool for Learning Inverse Theory; I.3. A Very Quick MatLab Tutorial; I.4. Review of Vectors and Matrices and Their Representation in MatLab; I.5. Useful MatLab Operations; I.5.1. Loops; I.5.2. Loading Data from a File; I.5.3. Plotting Data; I.5.4. Creating Character Strings Containing the Values of Variables; I.5.4 References; Chapter 1: Describing Inverse Problems
1.1. Formulating Inverse Problems1.1.1. Implicit Linear Form; 1.1.2. Explicit Form; 1.1.3. Explicit Linear Form; 1.2. The Linear Inverse Problem; 1.3. Examples of Formulating Inverse Problems; 1.3.1. Example 1: Fitting a Straight Line; 1.3.2. Example 2: Fitting a Parabola; 1.3.3. Example 3: Acoustic Tomography; 1.3.4. Example 4: X-ray Imaging; 1.3.5. Example 5: Spectral Curve Fitting; 1.3.6. Example 6: Factor Analysis; 1.4. Solutions to Inverse Problems; 1.4.1. Estimates of Model Parameters; 1.4.2. Bounding Values; 1.4.3. Probability Density Functions
1.4.4. Sets of Realizations of Model Parameters1.4.5. Weighted Averages of Model Parameters; 1.5. Problems; 1.5 References; Chapter 2: Some Comments on Probability Theory; 2.1. Noise and Random Variables; 2.2. Correlated Data; 2.3. Functions of Random Variables; 2.4. Gaussian Probability Density Functions; 2.5. Testing the Assumption of Gaussian Statistics; 2.6. Conditional Probability Density Functions; 2.7. Confidence Intervals; 2.8. Computing Realizations of Random Variables; 2.9. Problems; 2.9 References; Chapter 3: Solution of the Linear, Gaussian Inverse Problem, Viewpoint 1
3.1. The Lengths of Estimates3.2. Measures of Length; 3.3. Least Squares for a Straight Line; 3.4. The Least Squares Solution of the Linear Inverse Problem; 3.5. Some Examples; 3.5.1. The Straight Line Problem; 3.5.2. Fitting a Parabola; 3.5.3. Fitting a Plane Surface; 3.6. The Existence of the Least Squares Solution; 3.6.1. Underdetermined Problems; 3.6.2. Even-Determined Problems; 3.6.3. Overdetermined Problems; 3.7. The Purely Underdetermined Problem; 3.8. Mixed-Determined Problems; 3.9. Weighted Measures of Length as a Type of A Priori Information; 3.9.1. Weighted Least Squares
3.9.2. Weighted Minimum Length3.9.3. Weighted Damped Least Squares; 3.10. Other Types of A Priori Information; 3.10.1. Example: Constrained Fitting of a Straight Line; 3.11. The Variance of the Model Parameter Estimates; 3.12. Variance and Prediction Error of the Least Squares Solution; 3.13. Problems; 3.13References; Chapter 4: Solution of the Linear, Gaussian Inverse Problem, Viewpoint 2; 4.1. Solutions Versus Operators; 4.2. The Data Resolution Matrix; 4.3. The Model Resolution Matrix; 4.4. The Unit Covariance Matrix; 4.5. Resolution and Covariance of Some Generalized Inverses
4.5.1. Least Squares
Record Nr. UNINA-9910452072303321
Menke William  
Amsterdam ; ; Boston, : Elsevier/AP, 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Geophysical data analysis [[electronic resource] ] : discrete inverse theory / / William Menke
Geophysical data analysis [[electronic resource] ] : discrete inverse theory / / William Menke
Autore Menke William
Edizione [Matlab ed., 3rd ed.]
Pubbl/distr/stampa Amsterdam ; ; Boston, : Elsevier/AP, 2012
Descrizione fisica 1 online resource (331 p.)
Disciplina 551
Soggetto topico Geophysics - Measurement
Inverse problems (Differential equations) - Numerical solutions
Oceanography - Measurement
ISBN 1-283-73512-1
0-12-397784-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Front Cover; Geophysical Data Analysis: Discrete Inverse Theory; Copyright; Dedication; Preface; Reference; Companion Web Site; Contents; Introduction; I.1. Forward and Inverse Theories; I.2. MatLab as a Tool for Learning Inverse Theory; I.3. A Very Quick MatLab Tutorial; I.4. Review of Vectors and Matrices and Their Representation in MatLab; I.5. Useful MatLab Operations; I.5.1. Loops; I.5.2. Loading Data from a File; I.5.3. Plotting Data; I.5.4. Creating Character Strings Containing the Values of Variables; I.5.4 References; Chapter 1: Describing Inverse Problems
1.1. Formulating Inverse Problems1.1.1. Implicit Linear Form; 1.1.2. Explicit Form; 1.1.3. Explicit Linear Form; 1.2. The Linear Inverse Problem; 1.3. Examples of Formulating Inverse Problems; 1.3.1. Example 1: Fitting a Straight Line; 1.3.2. Example 2: Fitting a Parabola; 1.3.3. Example 3: Acoustic Tomography; 1.3.4. Example 4: X-ray Imaging; 1.3.5. Example 5: Spectral Curve Fitting; 1.3.6. Example 6: Factor Analysis; 1.4. Solutions to Inverse Problems; 1.4.1. Estimates of Model Parameters; 1.4.2. Bounding Values; 1.4.3. Probability Density Functions
1.4.4. Sets of Realizations of Model Parameters1.4.5. Weighted Averages of Model Parameters; 1.5. Problems; 1.5 References; Chapter 2: Some Comments on Probability Theory; 2.1. Noise and Random Variables; 2.2. Correlated Data; 2.3. Functions of Random Variables; 2.4. Gaussian Probability Density Functions; 2.5. Testing the Assumption of Gaussian Statistics; 2.6. Conditional Probability Density Functions; 2.7. Confidence Intervals; 2.8. Computing Realizations of Random Variables; 2.9. Problems; 2.9 References; Chapter 3: Solution of the Linear, Gaussian Inverse Problem, Viewpoint 1
3.1. The Lengths of Estimates3.2. Measures of Length; 3.3. Least Squares for a Straight Line; 3.4. The Least Squares Solution of the Linear Inverse Problem; 3.5. Some Examples; 3.5.1. The Straight Line Problem; 3.5.2. Fitting a Parabola; 3.5.3. Fitting a Plane Surface; 3.6. The Existence of the Least Squares Solution; 3.6.1. Underdetermined Problems; 3.6.2. Even-Determined Problems; 3.6.3. Overdetermined Problems; 3.7. The Purely Underdetermined Problem; 3.8. Mixed-Determined Problems; 3.9. Weighted Measures of Length as a Type of A Priori Information; 3.9.1. Weighted Least Squares
3.9.2. Weighted Minimum Length3.9.3. Weighted Damped Least Squares; 3.10. Other Types of A Priori Information; 3.10.1. Example: Constrained Fitting of a Straight Line; 3.11. The Variance of the Model Parameter Estimates; 3.12. Variance and Prediction Error of the Least Squares Solution; 3.13. Problems; 3.13References; Chapter 4: Solution of the Linear, Gaussian Inverse Problem, Viewpoint 2; 4.1. Solutions Versus Operators; 4.2. The Data Resolution Matrix; 4.3. The Model Resolution Matrix; 4.4. The Unit Covariance Matrix; 4.5. Resolution and Covariance of Some Generalized Inverses
4.5.1. Least Squares
Record Nr. UNINA-9910779175303321
Menke William  
Amsterdam ; ; Boston, : Elsevier/AP, 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Geophysical data analysis : discrete inverse theory / / William Menke
Geophysical data analysis : discrete inverse theory / / William Menke
Autore Menke William
Edizione [Matlab ed., 3rd ed.]
Pubbl/distr/stampa Amsterdam ; ; Boston, : Elsevier/AP, 2012
Descrizione fisica 1 online resource (331 p.)
Disciplina 551
Soggetto topico Geophysics - Measurement
Inverse problems (Differential equations) - Numerical solutions
Oceanography - Measurement
ISBN 1-283-73512-1
0-12-397784-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Front Cover; Geophysical Data Analysis: Discrete Inverse Theory; Copyright; Dedication; Preface; Reference; Companion Web Site; Contents; Introduction; I.1. Forward and Inverse Theories; I.2. MatLab as a Tool for Learning Inverse Theory; I.3. A Very Quick MatLab Tutorial; I.4. Review of Vectors and Matrices and Their Representation in MatLab; I.5. Useful MatLab Operations; I.5.1. Loops; I.5.2. Loading Data from a File; I.5.3. Plotting Data; I.5.4. Creating Character Strings Containing the Values of Variables; I.5.4 References; Chapter 1: Describing Inverse Problems
1.1. Formulating Inverse Problems1.1.1. Implicit Linear Form; 1.1.2. Explicit Form; 1.1.3. Explicit Linear Form; 1.2. The Linear Inverse Problem; 1.3. Examples of Formulating Inverse Problems; 1.3.1. Example 1: Fitting a Straight Line; 1.3.2. Example 2: Fitting a Parabola; 1.3.3. Example 3: Acoustic Tomography; 1.3.4. Example 4: X-ray Imaging; 1.3.5. Example 5: Spectral Curve Fitting; 1.3.6. Example 6: Factor Analysis; 1.4. Solutions to Inverse Problems; 1.4.1. Estimates of Model Parameters; 1.4.2. Bounding Values; 1.4.3. Probability Density Functions
1.4.4. Sets of Realizations of Model Parameters1.4.5. Weighted Averages of Model Parameters; 1.5. Problems; 1.5 References; Chapter 2: Some Comments on Probability Theory; 2.1. Noise and Random Variables; 2.2. Correlated Data; 2.3. Functions of Random Variables; 2.4. Gaussian Probability Density Functions; 2.5. Testing the Assumption of Gaussian Statistics; 2.6. Conditional Probability Density Functions; 2.7. Confidence Intervals; 2.8. Computing Realizations of Random Variables; 2.9. Problems; 2.9 References; Chapter 3: Solution of the Linear, Gaussian Inverse Problem, Viewpoint 1
3.1. The Lengths of Estimates3.2. Measures of Length; 3.3. Least Squares for a Straight Line; 3.4. The Least Squares Solution of the Linear Inverse Problem; 3.5. Some Examples; 3.5.1. The Straight Line Problem; 3.5.2. Fitting a Parabola; 3.5.3. Fitting a Plane Surface; 3.6. The Existence of the Least Squares Solution; 3.6.1. Underdetermined Problems; 3.6.2. Even-Determined Problems; 3.6.3. Overdetermined Problems; 3.7. The Purely Underdetermined Problem; 3.8. Mixed-Determined Problems; 3.9. Weighted Measures of Length as a Type of A Priori Information; 3.9.1. Weighted Least Squares
3.9.2. Weighted Minimum Length3.9.3. Weighted Damped Least Squares; 3.10. Other Types of A Priori Information; 3.10.1. Example: Constrained Fitting of a Straight Line; 3.11. The Variance of the Model Parameter Estimates; 3.12. Variance and Prediction Error of the Least Squares Solution; 3.13. Problems; 3.13References; Chapter 4: Solution of the Linear, Gaussian Inverse Problem, Viewpoint 2; 4.1. Solutions Versus Operators; 4.2. The Data Resolution Matrix; 4.3. The Model Resolution Matrix; 4.4. The Unit Covariance Matrix; 4.5. Resolution and Covariance of Some Generalized Inverses
4.5.1. Least Squares
Record Nr. UNINA-9910807477903321
Menke William  
Amsterdam ; ; Boston, : Elsevier/AP, 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui