  The paper presents an efficient method for simulating the tails of a target
variable Z=h(X) which depends on a set of basic variables X=(X_1, ..., X_n). To
this aim, variables X_i, i=1, ..., n are sequentially simulated in such a
manner that Z=h(x_1, ..., x_i-1, X_i, ..., X_n) is guaranteed to be in the tail
of Z. When this method is difficult to apply, an alternative method is
proposed, which leads to a low rejection proportion of sample values, when
compared with the Monte Carlo method. Both methods are shown to be very useful
to perform a sensitivity analysis of Bayesian networks, when very large
confidence intervals for the marginal/conditional probabilities are required,
as in reliability or risk analysis. The methods are shown to behave best when
all scores coincide. The required modifications for this to occur are
discussed. The methods are illustrated with several examples and one example of
application to a real case is used to illustrate the whole process.
