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Teaching GuideTerm Faculty of Computer Science |
| Mestrado Universitario en Bioinformática para Ciencias da Saúde |
 Subjects |
| Probability. statistics and elements of biomathematics |
| Contents |
| Identifying Data | 2019/20 | |||||||||||||
| Subject | Probability. statistics and elements of biomathematics | Code | 614522007 | |||||||||||
| Study programme |
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| Descriptors | Cycle | Period | Year | Type | Credits | |||||||||
| Official Master's Degree | 1st four-month period |
First | Obligatory | 6 | ||||||||||
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| Topic | Sub-topic |
| 1. Basic concepts of probability and statistics revisited. |
a. Probability. Random variables and main discrete and continuous distributions. Multivariate distributions. b. Statistical inference: estimation, hypothesis testing and confidence intervals. |
| 2. R statistical programming language revisited. | a. Introduction to R. First steps. Internal functions. Help in R. Functions, loops, vectors. Statistical functions. Plots. Recursivity. R studio. b. Main probability distributions in R. c. Introduction to simulation in R. d. Descriptive statistics in R. e. Hipothesis testing and confidence intervals with R. |
| 3. Linear statistical models. | a. The simple linear regression model. Basic assumptions. Estimation. Testing. Prediction. Model diagnostics. b. The multivariate linear regression model. Basic assumptions. Estimation. Testing. Prediction. Model diagnostics. c. Basic models in experimental desing. One-way and two-way Analysis of Varianza (ANOVA), with or without interaction. Basic assumptions. Estimation. Testing. Model diagnostics. d. The multiple testing problem. False discovery rate. |
| 4. Introduction to stochastic processes. | a. Simple random walk. b. Poisson process and renewal processes. Birth-death processes. c. Markov processes. Markov Chains. |
| 5. Introduction to resampling methods. | a. The uniform Bootstrap. Computing the bootstrap distribution: exact distribution and aproximated distribution using Monte Carlo. Examples. Aplication of the bootstrap for estimating the precision and the bias of an estimator. b. Variations of the uniform Bootstrap. Parametric Bootstrap, symmetrized Bootstrap and smoothed Bootstrap. Discussion and examples. c. Bootstrap methods to construct confidence intervals: percentile method, percentil-t method, simmetrized percentil-t method. Examples. Simulation studies . |
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