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MS1MAS - Mathematical Statistics

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)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
    ESPB 6.0 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
    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.
    Literature
    1. Milan Merkle, Mathematical Statistics - unpublished manuscript
    2. Milan Merkle: Probability and Statistics for engineers and students of engineering, Academic Mind, Belgrade 2010
    3. D.C. Montgomery, G.C. Runger, Applied statistics and probability for engineers, Wiley, 2010 (Original title)
    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
    Activites during lectures 0 Test paper 50
    Practical lessons 0 Oral examination 0
    Projects 20
    Colloquia 30
    Seminars 0