STOCHASTIC METHODS

Second cycle degree in DATA SCIENCE

Campus: PADOVA

Language: English

Teaching period: First Semester

Lecturer: PAOLO DAI PRA

Number of ECTS credits allocated: 6


Syllabus
Prerequisites: Basic notions of differential and integral calculus, linear algebra and probability.
Examination methods: Written exam
Course unit contents: 1. Probability reviews.
• discrete and continuous distributions
• random variables, expectation and conditional expectation
• approximation of probability distributions.

2. Markov chains and random walks
• Markov Chain and their stationary distribution
• Monte Carlo (MCMC), convergence of MCMC-based algorithms
• Electrical networks.

3. Random graphs
• Erdos-Renyi graphs: connectivity, giant component.
• Random regular graphs
• Dynamic graphs. Preferential attachment.