PřF:M3121 Probability and Statistics I - Course Information
M3121 Probability and Statistics I
Faculty of ScienceAutumn 2010
- 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)
- RNDr. Marie Forbelská, Ph.D. (lecturer)
Mgr. Jakub Čupera, 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
- Thu 14:00–15:50 M1,01017
- Timetable of Seminar Groups:
M3121/02: Mon 8:00–9:50 M5,01013, L. Zavadilová
M3121/03: Thu 12:00–13:50 M4,01024, J. Čupera
M3121/04: Thu 8:00–9:50 M5,01013, J. Čupera - 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
- Mathematical Biology (programme PřF, B-BI)
- Mathematics - Economics (programme PřF, M-AM)
- Mathematics (programme PřF, B-MA)
- Mathematics (programme PřF, M-MA)
- Mathematics (programme PřF, N-MA)
- 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, covariance, moments and their properties, covariance and correlation matrices, characteristic function of random vector. Limit theorems: Borel and Cantelli theorem, Cebyshev's inequality, Laws of large numbers, central limit theorem.
- 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
- Enrolment Statistics (Autumn 2010, recent)
- Permalink: https://is.muni.cz/course/sci/autumn2010/M3121