Probability and Statistics
Code  Completion  Credits  Range  Language 

BIKPST  Z,ZK  5  13KP+4KC  Czech 
 Lecturer:
 Daniel Vašata
 Tutor:
 Daniel Vašata
 Supervisor:
 Department of Applied Mathematics
 Synopsis:

Students are introduced to elements of probability thinking, ability of the synthesis both prior and posterior information and use to work with random variables. They will be able to apply correctly basic models of the distribution of random variables and to solve applied probability problems in the area of informatics and computer science. Using statistical inference methods, they master methods of statistical inference to estimate unknown population parameters on the basis of sample. They get acquainted with basic methods of the determination of possible statistical dependence of two or more random variables.
 Requirements:

Basics of combinatorics and mathematical analysis.
 Syllabus of lectures:

1. Probability: Random event, event space structure, probability of a random event and its basic properties. Conditional probability: Dependent and independent events, Bayes theorem.
2. Random variable: Distribution function of a random variable, continuous and discrete distributions, quantiles, median. Characteristics of position and shape: Mean value, variance, general moments, kurtosis and skewness.
3. Overview of basic distributions: binomial, Poisson, uniform, normal, exponential. Their basic properties. Probability applications. Hash functions, probabilistic algorithms.
4. Random vector: Joint and marginal statistics, correlation coefficient, dependence and independence of random variables. Descriptive statistics: Classification and processing of data sets, characteristics of position, variance, and shape, sampling moments, graphical representation of data.
5. Random sampling: Simple and stratified sampling, their distributions, basic sampling statistics, sample mean and variance, distributions (tdistribution, Fdistribution, chi square). Parameter estimation: Confidence interval, point estimation, methods.
6. Hypothesis testing: Testing strategy, mean value and variance tests, some of their modifications. Application of statistical testing in CS. Nonparametric tests: Comparing distributions, Wilcoxon test, SmirnovKolmogorov test, goodnessoffit test.
7. Analysis of variance: Oneway and twoway classification, normality testing. Correlation and regression analysis: Linear and quadratic regression, sample correlation.
 Syllabus of tutorials:

1. Elements of probability. Conditional probability. Random variable. Basic characteristics of random variables. Using basic distributions. Calculations of random variable characteristics. Hash functions.
2. Multidimensional random variables. Processing of sets of data. Random sampling. Parameter estimation. Hypotheses testing. Nonparametric tests. Correlation analysis.
 Study Objective:

The goal of the module is to deliver students the standard basics of the probability theory and statistics while paying attention to typical applications in informatics.
 Study materials:

1. Johnson, J. L. ''Probability and Statistics for Computer Science''. WileyInterscience, 2008. ISBN 0470383429.
2. Li, X. R. ''Probability, Random Signals, and Statistics''. CRC, 1999. ISBN 0849304334.
 Note:
 Further information:
 https://courses.fit.cvut.cz/BIPST/
 Timetable for winter semester 2019/2020:
 Timetable is not available yet
 Timetable for summer semester 2019/2020:
 Timetable is not available yet
 The course is a part of the following study plans:

 Bc. Programme Informatics, Part Time Form of Study, in Czech, Version 2015  2020 (compulsory course in the program)
 Bc Branch Security and Information Technology, PartTime Form, in Czech, Version 2015 to 2020 (compulsory course in the program)
 Bc.Branch WSI, Specialization Software Engineering, PartTime Form, Versionverze 2015  2020 (compulsory course in the program)