Syllabus
* Statistical data processing of experimental data classical probability, conditional probability, Bayes theorem, random variable, moments of random variable, probability density function and distribution function, examples of probability distributions (discrete,continuous), random vector, estimates and their bias, correlation and covariance, correlation coefficients. Moment-generating function.
* Estimation of parameters of models maximum likelihood estimation, least square method (general linear model), normal equations and their solution, Singular Value Decomposition, examples, estimation of parameters of nonlinear models, Marquardt method, goodness of fit, confidence intervals estimation, non-parametric models.
* Random processes
Stationary and ergodic processes, convolution, Fourier transform, power spectrum, Wiener-Khinchin theorem, data sampling, Nyquist frequency, discrete Fourier transform, spectral analysis
* Examples of data processing procedures.
Annotation
The aim of the course is to give introduction to statistical data processing used in physics in general with emphasis on plasma and surface physics. The covered topics are: examples of basic probability distributions, data processing methods, estimation of parameters of linear and nonlinear models and introduction to random processes.
Utilization of presented methods will be shown using examples from surface and plasma physics.