Cristina Solares

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In this paper we analyze the problem of learning and updating of uncertainty in Dirichlet models, where updating refers to determining the conditional distribution of a single variable when some evidence is known. We first obtain the most general family of prior-posterior distributions which is conjugate to a Dirichlet likelihood and we identify those(More)
In this paper we show how Bayesian network models can be used to perform a sensitivity analysis using symbolic, as opposed to numeric, computations. An example of damage assessment of concrete structures of buildings is used for illustrative purposes. Initially, normal or Gaussian Bayesian network models are described together with an algorithm for(More)
The paper presents an efficient computational method for estimating the tails of a target variable Z which is related to other set of bounded variables X = is guaranteed to be in the tail of Z. The: method is shown to be very useful to perform an uncertainty analysis of Bayesian networks, when very large confidence intervals for the marginal/conditional(More)
The paper presents an efficient method for simulating the tails of a target variable Z = h(X) which depends on a set of ba-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 meth­ ods are(More)
In this paper we show how the mathematical and engineering points of view are complementary and help to model real problems that can be stated as systems of linear inequalities. The Γ-algorithm, which is used to analyze the compatibility of linear systems of inequalities, is applied to solve different engineering problems involving parametric inequality(More)
We can perform inference in Bayesian be­ lief networks by enumerating instantiations with high probability thus approximating the marginals. In this paper, we present a method for determining the fraction of in­ stantiations that has to be considered such that the absolute error in the marginals does not exceed a predefined value. The method is based on(More)