Bayesian inference is a probabilistic approach to the problem of drawing conclusions from observed data. Its main challenge is computational, which the Bayesian community tends to …
A key aspect of intelligent systems is their capacity to reason under uncertainty. This task involves calculating probabilities of relevant variables while considering any …
Probabilistic inference is a central task in intelligent systems, enabling reasoning under uncertainty across domains such as artificial intelligence, medical diagnosis, and …
Within the realm of probabilistic graphical models, message-passing algorithms offer a powerful framework for efficient inference. When dealing with discrete variables, these …