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Teaching GuideTerm Faculty of Science |
| Grao en Nanociencia e Nanotecnoloxía |
 Subjects |
| Numerical and Statistical Methods |
| Contents |
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| Identifying Data | 2022/23 | |||||||||||||
| Subject | Numerical and Statistical Methods | Code | 610G04013 | |||||||||||
| Study programme |
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| Descriptors | Cycle | Period | Year | Type | Credits | |||||||||
| Graduate | 1st four-month period |
Second | Obligatory | 6 | ||||||||||
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| Topic | Sub-topic |
| Unit 0: Introduction to numerical methods | Introduction to numerical methods. Erros. |
| Unit 1: Numerical resolution of linear systems and numerical approximation fo eigenvalues |
- Direct methods (LU, Cholesky) - Iterative methods (Jacobi, Gauss-Seidel) - Aproximation of eigenvalues:: QR - Aplications |
| Unit 2: Numerical resolution of non-linear equations |
- Non-linear equations (bisection, Newton and variant, functional iteration) - Non-linear systems (functional iteration, Newton) - Aplications |
| Unit 3: Interpolation, numerical derivation and integration | - Interpolation (Lagrange, Chebyshev, Splines) - Numerical derivation - Numerical integration (middle point, trapezoid, Simpson, gaussian quadrature) - Aplications |
| Unit 4. Basic concepts on probability theory |
- Probability formulas - Conditional probability and independent events - Bayes' theorem |
| Unit 5. Random variables | - Discrete and continuos variables - Normal distribution and Central Limit Theorem - Applications in Nanoscience and Nanotechnology |
| Unit 6. Introduction to Statistical Inference |
- Estimators and sampling distributions - Linear regression - Statistical analysis software tools |
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