Syllabus
* 1. Introduction into probability theory and statistics.
Random processes, ergodic processes, distribution function, probability density fuction. Characteristic function, statistical moments, correlation function, power spectrum, spectral density function, Wiener - Khinchin theorem, characteristics of stochastic processes - errors of estimation. Composed statistical systems, dispersion theorem.
* 2. Using random processes for system identification - principles.
Methods for measurements of statistical characteristics. Fluctuations in physics, electrical noise. Evolution of concepts for studying fluctuations, Brownian motion, Langevin equation. Physical quantities for description of a fluctuating system. Types of noise in physical systems. Dynamics of a system and power spectral density.
* 3. Introduction into information theory.
Uncertainty and entropy, entropy of source, channel capacity, noiseless channel, channel with noise, information loss, rate of transmission, signal sampling, Gabor's theorem, information content of a signal, signal to noise ratio.
Annotation
Stochastic processes and quantities, statistical characteristics, Wiener-Khinchin theorem, cumulative statistical systems, dispersion theorem. Evolution of concepts for studying fluctuations, Brownian motion, Langevin equation, noise.
Introduction into information theory, uncertainty and entropy, distorted signal, information loss, rate of transmission, Gabor's theorem, signal sampling, signal to noise ratio.