MU:MU11185 Applied Statistics II - Course Information

MU11185 Applied Statistics II

Extent and Intensity

2/1/0. 3 credit(s). Type of Completion: zk (examination).

Teacher(s)

RNDr. Jaroslav Bradík (lecturer)

Guaranteed by

RNDr. Oldřich Stolín, Ph.D.
Mathematical Institute in Opava

Timetable

Wed 13:05–14:40 203

• Timetable of Seminar Groups:

MU11185/01: Wed 14:45–15:30 203, J. Bradík

Prerequisites

( MU20027 Applied Statistics I || MU11160 Applied Statistics ) && TYP_STUDIA(B)
The student is supposed to be acquainted with the material covered in Applied statistics.

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 Methods and Modelling (programme MU, Bc-M)
• Mathematical Methods in Economics (programme MU, Bc-M)
• Mathematical Methods in Risk Management (programme MU, Bc-M)
• General Mathematics (programme MU, Bc-M)

Course objectives

The course Applied Statistics consists of lectures and tutorials whose goal is to introduce students to Bayesian statistics and its practical methods.

Syllabus

• 1. Statistical models.
  2. The geometry of the space of probability distributions.
  3. Singular statistical models.
  4. Variational Bayesian methods.
  5. Hierarchical models
  6. Pseudo-Bayesian methods: Expectation-maximization algorithm. Empirical Bayesian methods.
  7. First encounter with stochastic differential equations.
  8. Kalman filters.
  9. Dynamic filters.
  10. Purposeful control under random influences.
  

Literature

required literature
• Christian P. Robert. The Bayesian choice: From decision-theoretic foundations to computational implementation. New York, 2007. info

recommended literature
• Sumio Watanabe. Algebraic geometry and statistical learning theory. New York, 2009. ISBN 9780521864671. info
• Amari, Shun-ichi and Nagaoka, Hiroshi. Methods of Information Geometry. Oxford University Press, AMS, 2007. info
• Christian P. Robert and George Casella. Monte Carlo statistical methods. New York, 2004. info

not specified
• Karl Friston and Klaas Stephan and Baojuan Li and Jean Daunizeau. Generalised Filtering, Mathematical Problems in Engineering, Article ID 621670. 2010. info

Language of instruction

Czech

Further comments (probably available only in Czech)

The course can also be completed outside the examination period.

Teacher's information

Requirements to pass the course:
Satisfactory completion of all assigned exercises with at most two exceptions.

Activity	Difficulty [h]
Cvičení	10
Domácí příprava na výuku	24
Přednáška	20
Příprava na zápočet	8
Příprava na zkoušku	16
Summary	78

• Enrolment Statistics (Summer 2022, recent)
  
• Permalink: https://is.slu.cz/course/sumu/summer2022/MU11185