Probabilistic Graphical Models

  • Arhant, Y. (Participant)
  • Aleksandra Pizurica (Organiser)

Activity: Participating in or organising an event (conference, measurement campaign, ...) › Attending a course and passing the exam

Description

Probabilistic Graphical Models

Additional Description

Probabilistic graphical models are powerful tools for representing complex inference problems and incorporating uncertainty into the reasoning process. As such, they find numerous applications in many domains, including machine learning, computer vision, natural language processing and computational biology. Incorporating uncertainties into reasoning and decision-making processes is especially important in high-stakes applications (e.g., health), where data is scarce, or the model structure is uncertain. The course gives a strong theoretical basis as well as practical insights into probabilistic graphical models and the corresponding inference mechanisms.
Period1 Feb 2022 → 1 Jul 2022
Event typeCourse
OrganiserUniversity of Ghent
LocationGhent, BelgiumShow on map
Degree of RecognitionInternational

Keywords

  • Probabilistic graphical models
  • Bayesian networks
  • Markov Random Fields
  • Bayesian Inference
  • Markov Chain Monte Carlo amplers
  • Iterated conditional modes
  • Message passing
  • Belief propagation
  • Loopy Belief propagation
  • Bethe Approximation
  • Junction trees
  • Expectation Propagation
  • Structure Learning