Which method is employed when determining relationships using probabilistic models?

The Bayesian Network is a powerful probabilistic model used to represent and analyze relationships among various variables. It employs directed acyclic graphs where nodes symbolize random variables and edges represent probabilistic dependencies between these variables. This model enables the calculation of the likelihood of certain outcomes based on known information and helps in inferring the probabilities of unknown variables when some data is observed.

For instance, in a medical diagnosis scenario, a Bayesian Network can help calculate the probability of a disease given certain symptoms, by organizing and quantifying the relationships and dependencies between symptoms, diseases, and other relevant factors. This capability to handle uncertainty and make inferences is a key reason why Bayesian Networks are particularly effective in scenarios that rely on probabilistic reasoning.