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.