Subject description - AE2M01PMS

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AE2M01PMS Probability and Statistics Extent of teaching:4+2
Guarantors:  Roles:P,V Language of
teaching:
EN
Teachers:  Completion:Z,ZK
Responsible Department:13101 Credits:8 Semester:Z

Anotation:

The course covers probability and basic statistics. First classical probability is introduced, then theory of random variables is developed including examples of the most important types of discrete and continuous distributions. Next chapters contain moment generating functions and moments of random variables, expectation and variance, conditional distributions and correlation and independence of random variables. Statistical methods for point estimates and confidence intervals are investigated.

Study targets:

The aim of the course is to introduce students to basics of probability and statistics.

Course outlines:

1. Events and probability.
2. Sample spaces.
3. Independent events, conditional probability, Bayes' formula.
4. Random variable, distribution functin, quantile function, moments.
5. Independence of random variables, sum of independent random variables.
6. Transformation of random variables.
7. Random vector, covariance and correlation.
8. Chebyshev's inequality and Law of large numbers.
9. Central limit theorem.
10. Random sampling and basic statistics.
11. Point estimation, method of maximum likehood and method of moments, confidence intervals.
12. Test of hypotheses.
13. Testing of goodness of fit.

Exercises outline:

1. Events and probability.
2. Sample spaces.
3. Independent events, conditional probability, Bayes' formula.
4. Random variable, distribution functin, quantile function, moments.
5. Independence of random variables, sum of independent random variables.
6. Transformation of random variables.
7. Random vector, covariance and correlation.
8. Chebyshev's inequality and Law of large numbers.
9. Central limit theorem.
10. Random sampling and basic statistics.
11. Point estimation, method of maximum likehood and method of moments, confidence intervals.
12. Test of hypotheses.
13. Testing of goodness of fit.

Literature:

[1] Papoulis, A.: Probability and Statistics, Prentice-Hall, 1990.
[2] Stewart W.J.: Probability, Markov Chains, Queues, and Simulation: The Mathematical Basis of Performance Modeling. Princeton University Press 2009.

Requirements:

The requirement for receiving the credit is an active participation in the tutorials.

Webpage:

http://math.feld.cvut.cz/helisova/01pstimfe.html

Subject is included into these academic programs:

Program Branch Role Recommended semester
MEKME1 Wireless Communication P 1
MEKME5 Systems of Communication P 1
MEKME4 Networks of Electronic Communication P 1
MEKME3 Electronics P 1
MEKME2 Multimedia Technology P 1
MEOI1 Artificial Intelligence V 1
MEOI5NEW Software Engineering V 1
MEOI5 Software Engineering V 1
MEOI4 Computer Graphics and Interaction V 1
MEOI3 Computer Vision and Image Processing V 1
MEOI2 Computer Engineering V 1
MEEEM1 Technological Systems V 1
MEEEM5 Economy and Management of Electrical Engineering V 1
MEEEM4 Economy and Management of Power Engineering V 1
MEEEM3 Electrical Power Engineering V 1
MEEEM2 Electrical Machines, Apparatus and Drives V 1
MEKYR4 Aerospace Systems V 1
MEKYR1 Robotics V 1
MEKYR3 Systems and Control V 1
MEKYR2 Sensors and Instrumentation V 1


Page updated 17.6.2019 14:52:47, 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)