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Statistics II

General data

Course ID:	1100-5FM11
Erasmus code / ISCED:	(unknown) / (unknown)
Course title:	Statistics II
Name in Polish:	Statystyka II
Organizational unit:	Faculty of Physics
Course groups:	(in Polish) ZFBM, II stopień; Fizyka medyczna (in Polish) ZFBM, II stopień; Neuroinformatyka
ECTS credit allocation (and other scores):	7.00 Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load. view allocation of credits
Language:	English
Prerequisites (description):	The goal is to familiarize the student with modern methods used in statistical reasoning and model building with emphasis on Bayesian methods. During Computer Lab time students will get hands on experience with different methods of statistical inference using high level symbolic language Mathematica (no previous knowledgeof Mathematica is assumed)
Mode:	Classroom
Short description:	Course: modern Bayesian approach to statistics. Linear models and Stochastic Series will be discussed. Lab: using Mathematica Language students will analise a variety of models and explore different method of exploring experimental data
Full description:	1) difference between probability theory and statistics. Three schools: classical, Bayesian, game theoretic 2) Basic probability methods: Fourier transform, convolution, moment generating functions 3) Basic distributions: constant, binomial, Poisson, Gaussian 4) Stable distributions; Levy distributions, heavy tailed distributions 5) Maximum likelihood and it's Bayesian interpretation 6) Chi squared - the case of systematic errors 7) Monte Carlo parameter error estimation 8) Contingency tables 9) Linear models - ANOVA, factor analysis, discrimination analysis 10) Stochastic series, Wiener-Khinchin theorem 11} Random walks, ARIMA models
Bibliography:	UNDERSTANDING AND USING ADVANCED STATISTICS Jeremy Foster Emma Barkus Christian Yavorsky FUNDAMENTALS OF PROBABILITY AND STATISTICS FOR ENGINEERS T.T. Soong Jayanta K. Ghosh Mohan Delampady Tapas Samanta An Introduction to Bayesian Analysis Theory and Methods
Learning outcomes:	Student should confidently use and understand modern statistics methods
Assessment methods and assessment criteria:	Successful completion of Lab Oral exam

Classes in period "Winter semester 2023/24" (past)

Time span:	2023-10-01 - 2024-01-28	Selected timetable range: weekly course term Navigate to timetable MO TU CW WYK W TH CW FR
Type of class:	Classes, 60 hours, 20 places more information Lecture, 30 hours, 20 places more information
Coordinators:	Katarzyna Grabowska
Group instructors:	Katarzyna Grabowska, Paweł Jędrejko
Students list:	(inaccessible to you)
Examination:	Course - Examination Lecture - Examination

Classes in period "Winter semester 2024/25" (future)

Time span:	2024-10-01 - 2025-01-26	Selected timetable range: weekly course term Navigate to timetable MO TU CW WYK W TH CW FR
Type of class:	Classes, 60 hours, 20 places more information Lecture, 30 hours, 20 places more information
Coordinators:	Katarzyna Grabowska
Group instructors:	(unknown)
Students list:	(inaccessible to you)
Examination:	Course - Examination Lecture - Examination

Course descriptions are protected by copyright.
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