2024
Computacional Statistics
Name: Computacional Statistics
Code: MAT13612M
6 ECTS
Duration: 15 weeks/156 hours
Scientific Area:
Mathematics
Teaching languages: Portuguese
Languages of tutoring support: Portuguese, English
Regime de Frequência: Presencial
Sustainable Development Goals
Learning Goals
Objectives:
To focus the importance of the statistical models in different scientific areas.
Understand and correctly apply the following statistical methods that need intensive use of the computer: Newton-Raphson type algorithms, Monte Carlo, EM algorithm, re-sampling and MCMC.
Competences:
To familiarize the students with the computational treatment of data.
To identify in a real situation, which the more appropriate theoretical model. To relate the several theoretical models.
To develop simulation algorithms and computational techniques used in statistics.
To know the main foundations relating to the environment R.
To dominate techniques of parametric and nonparametric statistical inference.
To generate pseudo-random numbers and random variables for simulation methods.
To understand and be able to apply the method of bootstrap resampling.
To focus the importance of the statistical models in different scientific areas.
Understand and correctly apply the following statistical methods that need intensive use of the computer: Newton-Raphson type algorithms, Monte Carlo, EM algorithm, re-sampling and MCMC.
Competences:
To familiarize the students with the computational treatment of data.
To identify in a real situation, which the more appropriate theoretical model. To relate the several theoretical models.
To develop simulation algorithms and computational techniques used in statistics.
To know the main foundations relating to the environment R.
To dominate techniques of parametric and nonparametric statistical inference.
To generate pseudo-random numbers and random variables for simulation methods.
To understand and be able to apply the method of bootstrap resampling.
Contents
1. Statistical modelling. Common Statistical models. Adjustment non-parametric tests. Independence tests and uniformity tests. Graphics methods.
2. Maximum Likelihood estimation and the EM algorithm (with resource to numerical methods).
3. Uniform pseudorandom numbers generaton. Pseudorandom numbers generation with a specified distribution.
4. Resampling methods. Monte Carlo Method. Bootstrap and Jackknife.
5. Markov Chains Monte Carlo Methods (MCMC), Gibbs algorithm and Metroplolis-Hasting algorithm.
6. Applications and use of statistical software.
2. Maximum Likelihood estimation and the EM algorithm (with resource to numerical methods).
3. Uniform pseudorandom numbers generaton. Pseudorandom numbers generation with a specified distribution.
4. Resampling methods. Monte Carlo Method. Bootstrap and Jackknife.
5. Markov Chains Monte Carlo Methods (MCMC), Gibbs algorithm and Metroplolis-Hasting algorithm.
6. Applications and use of statistical software.
Teaching Methods
Theoretical-practical lessons mainly lectured in a blackboard, with e-learning tools and transparencies.
Computational applications accomplishment.
Introduction to theoretical concepts appealing to different areas of applications to illustrate the importance of course contents. Exercises with emphasis in the resolution of real problems, to motivate interest in the course and to demonstrate its utility.
To emphasize the critical analysis and interpretation of data, appealing to software outputs as much as possible.
Computational applications accomplishment.
Introduction to theoretical concepts appealing to different areas of applications to illustrate the importance of course contents. Exercises with emphasis in the resolution of real problems, to motivate interest in the course and to demonstrate its utility.
To emphasize the critical analysis and interpretation of data, appealing to software outputs as much as possible.
Assessment
To privilege continued evaluation carrying out one test (50%) plus two individual/group homework projects (50%). If continuous evaluation is not feasible for the student, a final examination (75%) is possible, but the individual / group project is still required although with lesser weight for final grade (25%). In the test, the student must have a grade greater than 8.
Teaching Staff
- Maria Manuela Melo Oliveira [responsible]
Certificações
