UE Advanced data analysis

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Degrees incorporating this pedagocial element :

Description

This course is based on the following plan:

- Chap I: Introduction: Why analyse data? Concept of probability. Frequentist/Bayesian analysis.

- Chap II: Elements of statistics: A few reminders about basic concepts in statistics. Description and transformation of random variables in several dimensions. Important distributions.

- Chap III: Stochastic processes: Important stochastic processes. Linear systems and frequency theory. Filtering.

- Chap IV: Monte-Carlo methods: Principles and implementation of Monte-Carlo simulation. Pseudo-random number generator for one or more dimensions.

- Chap V: Bayesian inference: Bayes' theorem. Marginalisation. MCMC method. Nuisance parameters. Evidence and model selection.

 - Chap VI: Frequentist analysis I: Testing a hypothesis: Principle and construction of a test. Linear test. Neural network. Decision trees.

 - Chap VII: Frequentist analysis II: Estimation: Maximum likelihood and chi-square methods Nuisance parameters and profile likelihoods.

Recommended prerequisite

 Level L3 (bachelor) data analysis course.