Navigation

13M081MAST - Mathematical Statistics

Course specification
Course title Mathematical Statistics
Acronym 13M081MAST
Study programme Electrical Engineering and Computing
Module Applied Mathematics, Audio and Video Communications, Audio and Video Technologies, Biomedical and Environmental Engineering, Biomedical and Nuclear Engineering, Computer Engineering and Informatics, Electronics, Electronics and Digital Systems, Energy Efficiency, Information and Communication Technologies, Microwave Engineering, Nanoelectronics and Photonics, Power Systems - Networks and Systems, Power Systems - Renewable Energy Sources, Power Systems - Substations and Power Equipment, Signals and Systems, Software Engineering, System Engineering and Radio Communications
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 (3 or 6 credits), Mathematics 1 and Mathematics 2
    The goal Acquisition of knowledge about methods of mathematical statistics and their applications in problems of estimation,detection, classification and hypotheses testing in the framework of classical and Bayesian theory.
    The outcome A student will be able to use methods of mathematical statistics in areas of parameters estimation, testing hypotheses using classical and Bayesian paradigm, based on samples from a distribution or a random process.
    Contents
    Contents of lectures Short review of probability theory. Likelihood function. Parameters estimation. Testing hypotheses. Monte Carlo methods. Conditional distributions and conditional expectation. Bayesian theory and its applications. Linear regression. Logistic and other kinds of regression in classification problems. High dimensional statistics. Robust methods with statistical depth functions.
    Contents of exercises Study research work on a given topic
    Literature
    1. Probability and Statistics for engineers and students of engineering, fourth revised edition, Academic Mind 2016.
    2. D.C. Montgomery, G.C. Runger, Applied statistics and probability for engineers, Wiley, 2010 (Original title)
    3. Trevor Hastie, Robert Tibshirani, Jerome Friedman, The elements of statistical learning- Data Mining, Inference and Prediction, Second edition, Springer 2017 (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 supplemented with software demonstration. Presentations in groups or individually.
    Knowledge score (maximum points 100)
    Pre obligations Points Final exam Points
    Activites during lectures Test paper 30
    Practical lessons 20 Oral examination
    Projects
    Colloquia
    Seminars 50