Swedish web site
Bayesian LearningDF22500, 2015VT
|
|
Course plan
No of lectures
22 lecture hours + 8 computer labs hours
Recommended for
PhD students in Statistics, Computer Science and Cognitive Science.
The course was last given
Spring 2015
Goals
The course aims to give a solid introduction to the Bayesian approach to statistical inference, with a view towards applications in data mining and machine learning.
Prerequisites
Students admitted to the Master’s programme in Statistics and Data Mining
fulfill the admission requirements for the course.
Students not admitted to the Masters’ programme in Statistics and Data Mining
should have passed:
an intermediate course in probability and statistical inference
a basic course in mathematical analysis
a basic course in linear algebra
a basic course in programming
It also required to have a basic knowledge of linear regression, either as a
part of a statistics course, or as a separate course.
Organization
The course consists of lectures, exercise sessions, and computer labs. The
lectures are devoted to presentations of concepts and methods. Mathematically
oriented problems are solved in the exercise sessions. The computer labs are
used for practical applications of Bayesian inference.
Language of instruction: English.
The schedule will be travel-friendly.
Contents
The course aims to give a solid introduction to the Bayesian approach to statistical inference, with a view towards applications in data mining and machine learning. After an introduction to the subjective probability concept that underlies Bayesian inference, the course moves on to the mathematics of the prior-to-posterior updating in basic statistical models, such as the Bernoulli, normal and multinomial models. Linear regression and spline regression are also analyzed using a Bayesian approach. The course subsequently shows how complex models can be analyzed with simulation methods like Markov Chain Monte Carlo (MCMC). Bayesian prediction and marginalization of nuisance parameters is explained, and introductions to Bayesian model selection and Bayesian decision theory are also given.
Literature
Bayesian Data Analysis, 3rd edition.
Lecturers
Mattias Villani
Examiner
Mattias Villani
Examination
The course is examined by written reports on computer lab assignments and an individual project, which may be complemented with an oral exam.
Credit
6 ECTS credits.
Comments
The course is also given within the master programme Statistics and Data Mining.
Page responsible: Director of Graduate Studies
