probability distribution

The team derived their quantum Bayes' rule by maximizing the fidelity between two objects that represent the forward and the reverse process, in analogy with a classical joint probability distribution.

However, when simulations of a physical system using tried-and-true scientific techniques are available, researchers get a model of its probability distribution for free.

The MIT team's insight is that this probability distribution also defines a generative model upon which a classifier can be constructed.

The researchers have fed all the variables into the models, and developed a probability distribution for how conditions might develop in the future.

This is then used to generate a probability distribution function which consists of all the feasible and infeasible solutions that are within the constraints of the COP.