|Website:||website containing additional information|
|Period:||period 1 (week 36 through 45, i.e., 8-9-2011 through 11-11-2011; retake week 1)|
|Participants:||up till now 32 subscriptions|
|Schedule:||Official schedule representation can be found in Osiris|
|Teachers:||Dit is een oud rooster!
How long after infection will we detect classical swine fever on this farm?
Human experts have to make judgments and decisions based on uncertain, and often even conflicting, information. To support these complex decisions, knowledge-based systems should be able to cope with this type of information. For this reason, formalisms for representing uncertainty and algorithms for manipulating uncertain information are important research subjects within the field of Artificial Intelligence. Probability theory is one of the oldest theories dealing with the concept of uncertainty; it is therefore no surprise that the applicability of this mathematical theory as a model for reasoning under uncertainty plays an important role.
In this course, we will consider probabilistic models for manipulating uncertain information in knowledge-based systems. More specifically, we will consider the theory underlying the framework of probabilistic networks, and discuss the construction of such networks for real-life applications.
|Literature:||1. Syllabus 'Probabilistic Reasoning', available at the Science student desk (BBL 1.84b);|
2. Studymanual, available online (see website with additional information);
3. Course slides, also available online.
|Course form:||Lectures (twice a week).|
|Exam form:||one practical assignment (10%) and one written exam (90%). (Note that the OSIRIS page incorrectly shows different weights!)|
|Minimum effort to qualify for 2nd chance exam:||Om aan de aanvullende toets te mogen meedoen moet de oorspronkelijke uitslag minstens 4 zijn.|
|Description:||In this course, we will consider the theory and applicability of probabilistic networks. The course roughly consists of three parts. As an introduction to probabilistic networks, the first part of the course deals with independence relations and their graphical represenation by means of undirected and directed graphs. The second part introduces the probabilistic network as a compact representation of a probability distribution on a set of statistical variables; in addition, the Pearl algorithm for computing probabilities from a probabilistic network is discussed. The algorithm allows for calculating the probability of any value of an arbitrary variable in the network, with or without incorporating observations for one or more variables. The third part of this course concerns the construction of probabilistic networks for real-life applications. Topics covered include both automated construction of networks from data, and handcrafting the network with the help of domain experts.|