Course detail
Statistics and Probability
FIT-MSPAcad. year: 2019/2020
Summary
of elementary concepts from probability theory and mathematical statistics.
Limit theorems and their applications. Parameter estimate methods and their
properties. Scattering analysis including post hoc analysis. Distribution
tests, tests of good compliance, regression analysis, regression model
diagnostics, non-parametric methods, categorical data analysis. Markov
decision-making processes and their analysis, randomized algorithms.
Supervisor
Learning outcomes of the course unit
Students
will extend their knowledge of probability and statistics, especially in the
following areas:
- Parameter
estimates for a specific distribution - simultaneous
testing of multiple parameters - hypothesis
testing on distributions - regression
analysis including regression modeling - nonparametric
methods - Markov
processes
Prerequisites
Foundations of differential and integral calculus.
Foundations
of descriptive statistics, probability theory and mathematical statistics.
Co-requisites
Not applicable.
Recommended optional programme components
Not applicable.
Recommended or required reading
Anděl, Jiří. Základy matematické statistiky. 3., opr. vyd. Praha: Matfyzpress, 2011. ISBN 978-80-7378-001-2.
Meloun M., Militký J.: Statistické zpracování experimentálních dat (nakladatelství PLUS, 1994).
FELLER, W.: An Introduction to Probability Theory and its Applications. J. Wiley, New York 1957. ISBN 99-00-00147-X
Hogg, V.R., McKean J.W. and Craig A.T. Introduction to Mathematical Statistics. Seventh Edition, 2012. Macmillan Publishing Co., INC. New York. ISBN-13: 978-0321795434 2013
Zvára K.. Regresní analýza, Academia, Praha, 1989
D. P. Bertsekas, J. N. Tsitsiklis. Introduction to Probability, Athena, 2008. Scientific
Planned learning activities and teaching methods
Not applicable.
Assesment methods and criteria linked to learning outcomes
Three tests will be written during the semester - 3rd, 6th and 11th week. The exact term will be specified by the lecturer. The test duration is 60 minutes. The evaluation of each test is 0-10 points.
Projected evaluated 0-10 points.
Final written exam - 60 points
Exam prerequisites:
The credit will be awarded to the one who meets the attendance conditions and whose total test scores will reach at least 15 points and project score at least 5 points. The points earned in the exercise are transferred to the exam.
Language of instruction
Czech
Work placements
Not applicable.
Aims
Introduction
of further concepts, methods and algorithms of probability theory, descriptive
and mathematical statistics. Development of probability and statistical topics
from previous courses. Formation of a stochastic way of thinking leading to
formulation of mathematical models with emphasis on information fields.
Specification of controlled education, way of implementation and compensation for absences
Participation
in lectures in this subject is not controlled
Participation
in the exercises is compulsory. During the semester two abstentions
are tolerated. Replacement of missed lessons is determined by the leading exercises.
Classification of course in study plans
- Programme MITAI Master's
specialization NADE , 1. year of study, winter semester, 6 credits, compulsory
specialization NBIO , 1. year of study, winter semester, 6 credits, compulsory
specialization NGRI , 1. year of study, winter semester, 6 credits, compulsory
specialization NNET , 1. year of study, winter semester, 6 credits, compulsory
specialization NVIZ , 1. year of study, winter semester, 6 credits, compulsory
specialization NCPS , 1. year of study, winter semester, 6 credits, compulsory
specialization NSEC , 1. year of study, winter semester, 6 credits, compulsory
specialization NEMB , 1. year of study, winter semester, 6 credits, compulsory
specialization NHPC , 1. year of study, winter semester, 6 credits, compulsory
specialization NISD , 1. year of study, winter semester, 6 credits, compulsory
specialization NIDE , 1. year of study, winter semester, 6 credits, compulsory
specialization NISY , 1. year of study, winter semester, 6 credits, compulsory
specialization NMAL , 1. year of study, winter semester, 6 credits, compulsory
specialization NMAT , 1. year of study, winter semester, 6 credits, compulsory
specialization NSEN , 1. year of study, winter semester, 6 credits, compulsory
specialization NVER , 1. year of study, winter semester, 6 credits, compulsory
specialization NSPE , 1. year of study, winter semester, 6 credits, compulsory
Type of course unit
Lecture
26 hours, optionally
Teacher / Lecturer
Syllabus
- Summary
of basic theory of probability: axiomatic definition of probability,
conditioned probability,
dependent and independent events, Bayes formula. - Summary
of discrete and continuous random variables: probability, probability
distribution density, distribution function and their properties, functional
and numerical characteristics of random variable, basic discrete and continuous distributions. - Discrete
and continuous random vector (distribution functions, characteristics,
multidimensional distribution). Transformation of random variables.
Multidimensional normal distribution. - Limit
theorems and their use (Markov and Chebyshev Inequalities, Convergence, Law of
Large Numbers, Central Limit Theorem) - Parameter
estimation. Unbiased and consistent estimates. Method of moments, Maximum
likelihood method, Bayesian approach - parameter estimates. - Analysis
of variance (simple sorting, ANOVA). Multiple comparison (Scheffy and Tukey
methods). - Testing
statistical hypotheses on distributions. Goodness of fit tests. - Regression
analysis. Creating a regression model. Test hypotheses on regression model
parameters. Comparison of regression models. Diagnostics. - Project assignment, demonstration of programs and tools for solving statistical problems.
- Nonparametric
methods for testing statistical hypotheses. - Analysis
of categorical data: contingency table, chi-square test, Fisher test. - Markov processes, Markov decision processes, and their analysis and applications.
- Introduction
to randomized algorithms and their use (Monte Carlo, Las Vegas, applications).
Exercise
34 hours, compulsory
Teacher / Lecturer
Syllabus
- Sets, relations, and their basic properties.
- Propositional calculus and its formal system.
- Repetition of the basic probability theory and statistics.
- Important distribution and their use in Limit theorems.
- Parameter estimate: properties, methods
- Analysis of variance (simple sorting, ANOVA), post hos analysis.
- Testing statistical hypotheses on distributions. Goodness of fit tests.
- Regression analysis. Creating a regression model. Test hypotheses on regression model parameters.
- Regression analysis. Test hypotheses on regression model parameters. Diagnostics.
- Nonparametric methods for testing statistical hypotheses.
- Analysis of categorical data: contingency table, chi-square test.
- Application and analysis of Markov processes and Markov decision processes.
- Introduction to randomized algorithms
- Sets, Cartesian product, relations, and functions. Properties and types of relations and functions. Congruence.
- Basic algebraic structures (group, Boolean algebra, lattice, field). Homomorfism.
- Propositional calculus. Syntax and semantics. Formal system for propositional calculus. Posts completeness theorem.
- Predicate logic. Syntax and semantics. Formal system for predicate logic. Gödels completeness theorem. Gödels incompleteness theorem.
Projects
5 hours, compulsory
Teacher / Lecturer
Syllabus
- Usage of tools for solving statistical problems (data processing and interpretation).
eLearning
eLearning: currently opened course