One aspect of the invention is the construction of mixtures of Bayesian
networks. Another aspect of the invention is the use of such mixtures of
Bayesian networks to perform inferencing. A mixture of Bayesian networks
(MBN) consists of plural hypothesis-specific Bayesian networks (HSBNs)
having possibly hidden and observed variables. A common external hidden
variable is associated with the MBN, but is not included in any of the
HSBNs. The number of HSBNs in the MBN corresponds to the number of states
of the common external hidden variable, and each HSBN is based upon the
hypothesis that the common external hidden variable is in a corresponding
one of those states. In one mode of the invention, the MBN having the
highest MBN score is selected for use in performing inferencing. In
another mode of the invention, some or all of the MBNs are retained as a
collection of MBNs which perform inferencing in parallel, their outputs
being weighted in accordance with the corresponding MBN scores and the MBN
collection output being the weighted sum of all the MBN outputs. In one
application of the invention, collaborative filtering may be performed by
defining the observed variables to be choices made among a sample of users
and the hidden variables to be the preferences of those users.
