Universität Osnabrück
Seminar: Basic methods of probabilistic reasoning - Details
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General information

Course number 8.3273
Semester SS 2018
Current number of participants 42
Home institute LE Cognitive Science
Courses type Seminar in category Offizielle Lehrveranstaltungen
First date Fri , 06.04.2018 10:00 - 12:00, Room: 93/E07
Performance record Assessment: presentation + quizzes
Sprache Englisch
Literatur As a general reference for the course, we will use

Daphne Koller, Nir Friedman.
Probabilistic graphical models: principles and techniques.
MIT press, 2009.
Contact Hours 2
ECTS points 4

Course location / Course dates

93/E07 Friday: 10:00 - 12:00, weekly (from 06/04/18) (14x)


Prerequisites: Basic knowledge in computer science and math,
in particular probability theory

Classical reasoning methods are too weak for many practical applications because they cannot deal with uncertainty. Various qualitative (e.g. non-monotonic reasoning) and quantitative (e.g. fuzzy reasoning, possibilistic reasoning) extensions have been proposed to overcome these shortcomings. In this course, we will discuss probabilistic methods that build up on probability theory. For instance, probabilistic logics extend classical logics by replacing the truth values 0 and 1 with the probability interval [0,1]. Reasoning results in classical logic are well founded by the laws of probability theory. However, in order to perform probabilistic reasoning in reasonable time, we often have to make independency assumptions. Probabilistic graphical models represent the independency structure graphically and offer various reasoning and learning algorithms that can exploit this structure. While Probabilistic graphical models can deal with large problems, their logical expressiveness is rather weak. Recent frameworks from the field of statistical relational learning like Markov Logic and ProbLog can offer a better tradeoff between expressiveness and efficiency.
The first part of the course will consist of lectures, where some of the main formalisms for probabilistic reasoning are introduced. This includes Bayesian and Markov networks, which are the main families of probabilistic graphical models, and propositional probabilistic logic. In particular, we will talk about the basic reasoning (variable elimination, belief propagation) and learning (parameter/structure) methods. In the second part of the course, participants will present advanced topics.