Subject description - B0B01PST

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B0B01PST Probability and Statistics Extent of teaching:4P+2S
Guarantors:Navara M. Roles:P Language of
teaching:
Teachers:Navara M. Completion:Z,ZK
Responsible Department:13101 Credits:7 Semester:Z,L

Anotation:

Basics of probability theory and mathematical statistics. Includes descriptions of probability, random variables and their distributions, characteristics and operations with random variables. Basics of mathematical statistics: Point and interval estimates, methods of parameters estimation and hypotheses testing, least squares method. Basic notions and results of the theory of Markov chains.

Study targets:

Basics of probability theory and their application in statistical estimates and tests. The use of Markov chains in modeling.

Course outlines:

1. Basic notions of probability theory. Kolmogorov model of probability. Independence, conditional probability, Bayes formula.
2. Random variables and their description. Random vector. Probability distribution function.
3. Quantile function. Mixture of random variables.
4. Characteristics of random variables and their properties. Operations with random variables. Basic types of distributions.
5. Characteristics of random vectors. Covariance, correlation. Chebyshev inequality. Law of large numbers. Central limit theorem.
6. Basic notions of statistics. Sample mean, sample variance. Interval estimates of mean and variance.
7. Method of moments, method of maximum likelihood. EM algorithm.
8. Hypotheses testing. Tests of mean and variance.
9. Goodness-of-fit tests.
10. Tests of correlation, non-parametic tests.
11. Discrete random processes. Stationary processes. Markov chains.
12. Classification of states of Markov chains.
13. Asymptotic properties of Markov chains. Overview of applications.

Exercises outline:

1. Elementary probability.
2. Kolmogorov model of probability. Independence, conditional probability, Bayes formula.
3. Mixture of random variables.
4. Mean. Unary operations with random variables.
5. Dispersion (variance). Random vector, joint distribution. Binary operations with random variables.
6. Sample mean, sample variance. Chebyshev inequality. Central limit theorem.
7. Interval estimates of mean and variance.
8. Method of moments, method of maximum likelihood.
9. Hypotheses testing. Goodness-of-fit tests.
10. Tests of correlation. Non-parametic tests.
11. Discrete random processes. Stationary processes. Markov chains.
12. Classification of states of Markov chains.
13. Asymptotic properties of Markov chains.

Literature:

[1] Wasserman, L.: All of Statistics: A Concise Course in Statistical Inference. Springer Texts in Statistics, Corr. 2nd printing, 2004.
[2] Papoulis, A., Pillai, S.U.: Probability, Random Variables, and Stochastic Processes. McGraw-Hill, Boston, USA, 4th edition, 2002.
[3] Mood, A.M., Graybill, F.A., Boes, D.C.: Introduction to the Theory of Statistics. 3rd ed., McGraw-Hill, 1974.

Requirements:

Linear Algebra, Calculus, Discrete Mathematics

Note:

A necessary condition for the assignment is active participation at seminars, successful test, and one homework. More info: http://cmp.felk.cvut.cz/~navara/stat/

Webpage:

http://cmp.felk.cvut.cz/~navara/stat/

Keywords:

probability theory, statistical estimate, hypotheses testing, Markov chain

Subject is included into these academic programs:

Program Branch Role Recommended semester
BPOI_BO_2018 Common courses P 3
BPOI4_2018 Computer Games and Graphics P 3
BPOI3_2018 Software P 3
BPOI2_2018 Internet things P 3
BPOI1_2018 Artificial Intelligence and Computer Science P 3
BPOI1_2016 Computer and Information Science P 3
BPOI_BO_2016 Common courses P 3
BPOI4_2016 Computer Games and Graphics P 3
BPOI3_2016 Software P 3
BPOI2_2016 Internet things P 3
BPKYR_2016 Common courses P 4


Page updated 6.12.2019 17:52:32, semester: Z,L/2020-1, L/2018-9, Z,L/2019-20, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)