A fully unsupervised Bayesian Belief Network model is created using data from the training database; and, the fully unsupervised Bayesian Belief Network is validated.

For instance, let us consider the Bayesian causal network of Figure 3.7.

A probabilistic network, in particular a Bayesion network, is used for control of a printing system in order to realize an adaptable printing system.

The computing device applies the parsed document and at least one likelihood vector to a Bayesian network.

A Bayesian network algorithm is used with the data to solve the network for the unknown variables and their associated uncertainties (725).

A simple example Bayesian network is provided in Figure 1.

Heuristics (14) are used to determine a structure and conditional probabilities for the Bayesian network.

Preferred embodiments of MEQA technology use a Bayesian Network to calculate the probability of an answer's correctness.

The Bayesian network has been developed by Cozy Cloud[7], member of the PerSoCloud consortium.

A linear programming algorithm (16) is used to solve the plurality of constraints, and to construct a tuned Bayesian network model.

The invention may facilitate information propagation in a Bayesian Network and may in particular reduce memory requirements and granularity errors.

A method of propagation includes detecting (401) a change in a first data value associated with a first node of the Bayesian Network.

Items of the domain of interest, as well as the ontologies that describe attributes of those items, are embedded into a Bayesian network.

The Bayesian network-based classifier may also be used to determine if two observations (e.g., two images) belong to the same category.

AdSense uses a Bayesian network with over 300 million edges to learn which ads to serve.[206]