Probability and Statistics
Code  Completion  Credits  Range  Language 

AE1M01MPS  Z,ZK  8  4+2  English 
 Lecturer:
 Tutor:
 Supervisor:
 Department of Mathematics
 Synopsis:

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.
 Requirements:

The requirement for receiving the credit is an active participation in the tutorials.
 Syllabus of lectures:

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.
 Syllabus of tutorials:

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.
 Study Objective:

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

[1] Papoulis, A.: Probability and Statistics, PrenticeHall, 1990.
[2] Stewart W.J.: Probability, Markov Chains, Queues, and Simulation: The Mathematical Basis of Performance Modeling. Princeton University Press 2009.
 Note:
 Further information:
 http://math.feld.cvut.cz/helisova/01pstimfe.html
 No timetable has been prepared for this course
 The course is a part of the following study plans:

 Cybernetics and Robotics  Robotics (elective course)
 Cybernetics and Robotics  Senzors and Instrumention (elective course)
 Cybernetics and Robotics  Systems and Control (elective course)
 Open Informatics  Artificial Intelligence (elective course)
 Open Informatics  Computer Engineering (elective course)
 Open Informatics  Computer Vision and Image Processing (elective course)
 Open Informatics  Computer Graphics and Interaction (elective course)
 Open Informatics  Software Engineering (elective course)
 Communications, Multimedia and Electronics  Wireless Communication (elective course)
 Communications, Multimedia and Electronics  Multimedia Technology (elective course)
 Communications, Multimedia and Electronics  Electronics (elective course)
 Communications, Multimedia and Electronics  Networks of Electronic Communication (elective course)
 Electrical Engineering, Power Engineering and Management  Technological Systems (compulsory course in the program)
 Electrical Engineering, Power Engineering and Management  Electrical Machines, Apparatus and Drives (compulsory course in the program)
 Electrical Engineering, Power Engineering and Management  Electrical Power Engineering (compulsory course in the program)
 Cybernetics and Robotics  Air and Space Systems (elective course)
 Communications, Multimedia and Electronics  Communication Systems (elective course)
 Open Informatics  New  Software Engineering (elective course)