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.

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

  1. Summary
    of basic theory of probability: axiomatic definition of probability,
    conditioned probability,
    dependent and independent events, Bayes formula.
  2. 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.
  3. Discrete
    and continuous random vector (distribution functions, characteristics,
    multidimensional distribution). Transformation of random variables.
    Multidimensional normal distribution.
  4. Limit
    theorems and their use (Markov and Chebyshev Inequalities, Convergence, Law of
    Large Numbers, Central Limit Theorem)
  5. Parameter
    estimation. Unbiased and consistent estimates. Method of moments, Maximum
    likelihood method, Bayesian approach - parameter estimates.
  6. Analysis
    of variance (simple sorting, ANOVA). Multiple comparison (Scheffy and Tukey
    methods).
  7. Testing
    statistical hypotheses on distributions. Goodness of fit tests.
  8. Regression
    analysis. Creating a regression model. Test hypotheses on regression model
    parameters. Comparison of regression models. Diagnostics.
  9. Project assignment, demonstration of programs and tools for solving statistical problems.
  10. Nonparametric
    methods for testing statistical hypotheses.
  11. Analysis
    of categorical data: contingency table, chi-square test, Fisher test.
  12. Markov processes, Markov decision processes, and their analysis and applications.
  13. Introduction
    to randomized algorithms and their use (Monte Carlo, Las Vegas, applications).

Fundamentals seminar

34 hours, compulsory

Teacher / Lecturer

Syllabus

  1. Sets, relations, and their basic properties.
  2. Propositional calculus and its formal system.
  3. Repetition of the basic probability theory and statistics.
  4. Important distribution and  their use in Limit theorems.
  5. Parameter estimate: properties, methods
  6. Analysis of variance (simple sorting, ANOVA), post hos analysis.
  7. Testing statistical hypotheses on distributions. Goodness of fit tests.
  8. Regression analysis. Creating a regression model. Test hypotheses on regression model parameters.
  9. Regression analysis. Test hypotheses on regression model parameters. Diagnostics.
  10. Nonparametric methods for testing statistical hypotheses.
  11. Analysis of categorical data: contingency table, chi-square test.
  12. Application and analysis of Markov processes and Markov decision processes.
  13. Introduction to randomized algorithms

Demo exercise focusing on algebra and logic (only the first two weeks -- 4-times 2 hours):
  1. Sets, Cartesian product, relations, and functions. Properties and types of relations and functions. Congruence.
  2. Basic algebraic structures (group, Boolean algebra, lattice, field). Homomorfism.
  3. Propositional calculus. Syntax and semantics. Formal system for propositional calculus. Posts completeness theorem.  
  4. Predicate logic. Syntax and semantics. Formal system for predicate logic. Gödels completeness theorem. Gödels incompleteness theorem.

Project

5 hours, compulsory

Teacher / Lecturer

Syllabus

  1.  Usage of tools for solving statistical problems (data processing and interpretation).

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