Learning Bayesian Networks probabilities from longitudinal data

Many real applications of Bayesian networks (BN’s)
concern problems in which several observations are collected
over time on a certain number of similar plants. This situation
is typical of the context of medical monitoring, in which several measurements of the relevant physiological quantities are
available over time on a population of patients under treatment,
and the conditional probabilities that describe the model are
usually obtained from the available data through a suitable
learning algorithm. In situations with small data sets for each
plant, it is useful to reinforce the parameter estimation process
of the BN by taking into account the observations obtained from
other similar plants. On the other hand, a desirable feature to
be preserved is the ability to learn individualized conditional
probability tables, rather than pooling together all the available
data. In this work we apply a Bayesian hierarchical model able
to preserve individual parameterization, and, at the same time,
to allow the conditionals of each plant to borrow strength from
all the experience contained in the data-base. A testing example
and an application in the context of diabetes monitoring will be
shown.