Home page

About course

Course specification
Course title Mathematical Statistics
Acronym MS1MAS
Study programme Electrical Engineering and Computing
Module Applied Mathematics
Type of study master academic studies
Lecturer (for classes) prof. dr Milan Merkle
Lecturer/Associate (for practice) doc. dr Bojana Mihailović
Lecturer/Associate (for OTC)
ESPB 6 Status elective
Condition Probability and Statistics in undergraduate studies at least one semester with at least 15 hours of theoretical lectures per week.
The goal The course is a continuation of the undergraduate course "Probability and Statistics". The objective is to study principles and methods of mathematical statistics as an applied science, and to learn how to apply them in problems of estimation, detection, classification and hypotheses testing, within the framework of classical or Bayesian statistics.
The outcome A student will be able to use methods of Mathematical statistics related to estimation of parameters and testing parametric and non-parametric hypotheses, using the classical and Bayesian approach, based on a sample from a distribution, or from a random process.
Contents of lectures A short survey of Probability theory. Likelihood function. Parameter estimation. Hypothesis testing. Monte Carlo methods. Bayesian theory.
Contents of exercises Exercises - solving examples and problems. Practical applications of software tools to solving applied problems.
1Milan Merkle, Matematička statistika -skripta, Milan Merkle, Mathematical Statistics - unpublished manuscript
2Milan Merkle: Verovatnoća i statistika za inženjere i studente tehnike, Akademska Misao Beograd 2010, Milan Merkle: Probability and Statistics for engineers and students of engineering, Academic Mind, Belgrade 2010
3D.C. Montgomery, G.C. Runger, Applied statistics and probability for engineers, Wiley, 2010
Number of hours per week during the semester / trimester / year
Lectures Exercises OTC Study and Research Other classes
3 1
Methods of teaching Classical lecturing, complemented by demonstrations of sofware tools. Student's presentations, indivudually or in groups.
Knowledge score (maximum points 100)
Pre obligations Points Final exam Points
Activities during lectures 0 Test paper 50
Practical lessons 0 Oral examination 0
Projects 20
Colloquia 30
Seminars 0