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Environmental data analysis with MatLab /
Environmental Data Analysis with MatLab is a new edition that expands fundamentally on the original with an expanded tutorial approach, new crib sheets, and problem sets providing a clear learning path for students and researchers working to analyze real data sets in the environmental sciences. Sinc...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam :
Elsevier Academic Press,
[2016]
|
| Edición: | Second edition. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Front Cover; Environmental Data Analysiswith MATLAB�; Copyright; Dedication; Contents; Preface; Advice on scripting for beginners; 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. Representing functions; 1.10. To loop or not to loop; 1.11. The matrix inverse; 1.12. Loading data from a file; 1.13. Plotting data; 1.14. Saving data to a file
- 1.15. Some advice on writing scripts1.15.1. Think before you type; 1.15.2. Name variables consistently; 1.15.3. Save old scripts; 1.15.4. Cut and paste sparingly; 1.15.5. Start small; 1.15.6. Test your scripts; 1.15.7. Comment your scripts; 1.15.8. Don't be too clever; Problems; Chapter 2: A first look at data; 2.1. 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 functions3.5. 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 data; Problems; References; 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 errorProblems; 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 data; 5.7. Smoothness as prior information; 5.8. Sparse matrices; 5.9. Reorganizing grids of model parameters; Problems; References; 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 transform6.5. 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; References; Chapter 7: The past influences the present; 7.1. Behavior sensitive to past conditions; 7.2. Filtering as convolution; 7.3. Solving problems with filters; 7.4. An example of an empirically-derived filter; 7.5. Predicting the future; 7.6. A parallel between filters and polynomials; 7.7. Filter cascades and inverse filters