Applied Mathematics

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Department of Applied Mathematics

Synopsis:

Random processes and real applications

Bayesian prediction model of stochastic system (distribution, equations)

Continuous and discrete model - simulation of dynamical systems

System state, state-space model, filtering (Kalman filter)

Estimation of model parameters - Bayes relation, reproducibility, exponential family of distributions

Estimation statistics of continuous and discrete model, on-line recalculation of statistics, point estimates

Estimation of models with non-Gaussian or non-categorical distribution reproducibility, point estimates

Prediction with Bayesian model

Finite interval control, dynamic programming, Riccati equations, algorithmization

Mixture models of distributions with continuous and discrete components, hierarchical mixtures

Estimation of mixture of distributions

Mixture estimation for clustering and classification

Hierarchical mixture estimation

Prediction with mixture model

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No time-table has been prepared for this course

The course is a part of the following study plans: