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Calculate the probability of variables given evidence. (e.g. P(A) and P(B)
given the evidence.)
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Support for multiple variables per node. This allows, for example, the direct specification
of mixtures of full covariance Gaussians.
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Calculate the joint probability of variables given evidence (e.g. P(A,B), P(B,C)
given the evidence)
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Relevance optimization. Only distributions relevant to a query are used.
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Calculate the log-likelihood of a case.
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Evidence propagation. Implicit evidence is inferred from any explicit evidence.
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Supports hard, soft/virtual and temporal evidence.
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Support for disconnected networks.
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Continuous nodes/variables (CLG distributions).
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Missing data support. (Including Dynamic Bayesian networks and nodes with multiple
variables.)
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Dynamic Bayesian networks (DBN) for modelling temporal or sequential data.
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