Probabilistic Modelling (Master’s Program: Urban Supercomputing) (as a part of Discrete and Probabilistic Models)

Entry requirements: basic knowledge in field of probability theory and mathematical statistics

Credits: 6

Course: Core

Language of the course: English

Objectives

  • to improve backgrounds in probability theory
  • to develop skills in probabilistic modelling and statistic assessment

Contents

-        Probabilistic models for random variables. Univariate random variable. (CDF and PDF. Probability distribution parameters estimation. Probabilistic interval. Confidence interval)

-        Probabilistic models for random variables. Multivariate random variable. (Regression and correlation analysis. Principal component analysis. Multidimentional interval estimates)

-        Probabilistic models for stochastic processes. Univariate stochastic process. Multivariate stochastic process. (Temporal stochastic process and random fields. Gaussian stochastic processes. Stationarity and non-stationarity. Ergodic processes. Markov processes. Dynamic system model. Regression models for stochastic processes. Correlation analysis of stochastic processes. ARMA model. Spectrum analysis)

Format

Lectures, seminars, practical classes.

Assessment

Grading: 30% participation in class discussions and/or individual presentation on a topic of interest, 60% results of practical tasks; 10% results of tests.

Additional opportunity to improve scores during the exam.