Prior Samples Icon

• Although a Bayesian network is usually learned from data without prior knowledge, any available domain knowledge can also be incorporated. You can use a fully specified network (structure and parameters), either a manually constructed or a machine-learned network, to generate virtual particles and mix them with the actual ones described in your data set.
• However, this requires to "weight" your prior, i.e., to define N Prior Samples, the number of particles that will be generated.
• Together with the actual data set of M observations, BayesiaLab can then learn a new network from the N+M cases.
• The  icon indicates that Prior Samples are in use.
• By clicking on the icon, you can remove the Prior Samples from the network.