Statistics II 1100-5FM11

1) difference between probability theory and statistics.
Three schools: classical, Bayesian, game theoretic
2) Basic probability methods: Fourier transform, convolution, moment generating functions
3) Basic distributions: constant, binomial, Poisson, Gaussian
4) Stable distributions; Levy distributions, heavy tailed distributions
5) Maximum likelihood and it's Bayesian interpretation
6) Chi squared - the case of systematic errors
7) Monte Carlo parameter error estimation
8) Contingency tables
9) Linear models - ANOVA, factor analysis, discrimination analysis
10) Stochastic series, Wiener-Khinchin theorem
11} Random walks, ARIMA models

Mode

Classroom

Prerequisites (description)

The goal is to familiarize the student with modern methods used in statistical reasoning and model building with emphasis on Bayesian methods. During Computer Lab time students will get hands on experience with different methods of statistical inference using high level symbolic language Mathematica (no previous knowledgeof Mathematica is assumed)

Course coordinators

Katarzyna Grabowska

Learning outcomes

Student should confidently use and understand modern statistics methods

Assessment criteria

Successful completion of Lab
Oral exam

Additional information

Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes:

Applications of Physics in Biology and Medicine, second cycle programme

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of 1100-5FM11 in USOSweb