### Answe=
r

A BBN is a compact representation of the Joint Probability Distribution =
(JPD) defined by its associated variables. Once you get this JPD, inference=
in a BN is simply computing a conditional probability of some subset of va=
riables in the network, conditioned on another subset. For small models ( &=
lt;10 binary variables) this is a trivial. In general, the "what is compute=
d" is straightforward, but impractical by brute force. The "how it is compu=
ted" is the subject of a large public literature on propagation and approxi=
mation methods.

The BayesiaLab=E2=80=99s probabilistic inference is based on the Junctio=
n Tree algorithm for exact Inference, and Importance Sampling for approxima=
te inference (when exact inference is impossible due to the BBN complexity)=
.

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