Probabilistic model checking and Markov decision processes (MDPs) form two interlinked branches of formal analysis for systems operating under uncertainty. These techniques offer a mathematical ...

For humans and machines, intelligence requires making sense of the world — inferring simple explanations for the mishmosh of information coming in through our senses, discovering regularities and ...

Looking to gain greater insights into surveys? This third part of a series on NLP and survey data explores Latent Dirichlet Allocation, a popular tool to ferret out elusive themes in text comments, ...

Probabilistic graphical models are a powerful technique for handling uncertainty in machine learning. The course will cover how probability distributions can be represented in graphical models, how ...

This study investigates the potential of probabilistic classification to enhance credit-scoring accuracy, with a focus on model validation through reliability thresholds. By quantifying prediction ...