Sluiten Help Print Cursus: INFOPROB INFOPROB Probabilistic reasoning Cursus informatie Cursuscode INFOPROB Studiepunten (EC) 7,5 Cursusdoelen After completing the course, the student recognises the strengths and weaknesses of the probabilistic graphical model framework for reasoning under uncertainty understands the relation between probabilistic independence and the graphical representation of a probabilistic network can compute probabilities from a probabilistic network understands Pearl’s algorithm for probabilistic inference understand the different patterns of reasoning allowed in probabilistic networks knows about the challenges of constructing probabilistic networks for real applications understands the benefits and drawbacks of parameterized models, and the details of Noisy-Or understands a basic algorithm for automated network construction from data knows the purpose of sensitivity analysis and understands the related functions and concepts Inhoud 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. Probability theory is one of the oldest theories dealing with the concept of uncertainty. In this course, probabilistic models for manipulating uncertain information in knowledge-based systems are considered. More specifically, the theory underlying the framework of probabilistic networks are considered, and the construction of such networks for real-life applications are discussed. Sluiten Help Print