M3121 Probability and Statistics I

Faculty of Science
Autumn 2011
Extent and Intensity
2/2/0. 4 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: z (credit).
Teacher(s)
prof. RNDr. Gejza Wimmer, DrSc. (lecturer)
Mgr. Jakub Čupera, Ph.D. (seminar tutor)
doc. Mgr. Jan Koláček, Ph.D. (seminar tutor)
Mgr. Lenka Zavadilová, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Gejza Wimmer, DrSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Wed 10:00–11:50 M1,01017, Wed 12:00–13:50 M1,01017
  • Timetable of Seminar Groups:
M3121/01: Wed 10:00–11:50 M4,01024, L. Zavadilová
M3121/02: Tue 16:00–17:50 M2,01021, J. Čupera
M3121/03: Wed 8:00–9:50 M5,01013, L. Zavadilová
M3121/04: Tue 18:00–19:50 M2,01021, J. Čupera
M3121/05: Mon 10:00–11:50 M2,01021, J. Koláček
Prerequisites
M2100 Mathematical Analysis II || FI:MB001 Calculus II
Differential and integral calculus of functions of n real variables. Basic knowledge of linear algebra.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The basic course of probability and mathematical statistics and introductory course for other theoretically oriented and applied stochastic subjects. The content of the course is axiomatical approach to probability theory, random variables and random vectors, probability distributions and characteristics of the distribution. The last part of the course is devoted to the laws of large numbers and to the central limit theorem. On the completion of this course, the student is expected to obtain sufficient mastery of basic probability theory to be able to study topics on statistical inference.
Syllabus
  • Elements of probability: axiomatic definition of probability, probability space, conditional probability, independence. Random variables: borel functions, definition of random variable, distribution function, discrete and continuous probability distributions, probability and density function, examples of discrete and continuous random variables, distribution of transformed random variables. Random vectors: joint distributions, independence, examples of multivariate distributions (multivariate normal and multinomial distributions), distribution of the sum and ratio of random variables, distributions derived from normal distribution, marginal distributions. Characteristics: expectation, variance.
Literature
  • Ash, R.B. and Doléans-Dade C.A. Probability and measure theory. Academic Press. San Diego.2000
  • MICHÁLEK, Jaroslav. Úvod do teorie pravděpodobnosti a matematické statistiky. Vyd. 1. Praha: Státní pedagogické nakladatelství, 1984, 204 s. info
  • Karr, A.F. Probability. Springer. 1992
  • Dupač, V. a Hušková, M.: Pravděpodobnost a matematická statistika. Karolinum. Praha 1999.
Teaching methods
Lectures: theoretical explanation with practical examples Excercises: solving problems for acquirement basic concepts, solving theoretical problems, solving simples tasks and also complicated problems, homeworks
Assessment methods
Lecture with a exercises. Active work in exercises. 2 written tests.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.
  • Enrolment Statistics (Autumn 2011, recent)
  • Permalink: https://is.muni.cz/course/sci/autumn2011/M3121