Multivariate Data Analysis

PART I:

1. Refresher probability theory and basic statistics

2. Markov Chains

3. Introduction to stochastic processes: Brownian motion, Poisson process, autocorrelation function, stationarity, estimation of correlation function, seasonality and trend,

4. Stochastic processes in time domain: ARMA processes, linear prediction (theory and computer exercise)

5. Stochastic processes in frequency domain: spectral density function, white noise, periodogram

PART II:

A course in advanced statistics about linear models, Bayesian inference, classification problems, Gaussian processes and Markov Chain Monte Carlo.

The assessment of the course consists of the following components:

• Lab assignments (pass/fail)

• Written or oral exam (weighting 100%)

Resit/ Repair opportunities: In case of insufficient results a repair option may exist in accordance with Article 2, Examination requirements, Clause 4, of the Implementation Regulations for:

• Written or oral exam: Resit (written or oral exam, depending on the number of students)

Disclaimer: information may change depending on unforeseen circumstances or measures (see: TER Art 29, sub 4).