Cristina Solares

Learn More
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 method for simulating the tails of a target variable Z = h(X) which depends on a set of basic variables X = (X1, ... , Xn)· To this aim, variables X;; i = 1, ... , n are sequentially simulated in such a manner that Z = h(xt, . . . ,x;_1,X;, ... ,Xn) is guaranteed to be in the tail of Z. When this method is difficult to apply,(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 = (Xi,. . . , X,) by an increasing (decreasing) relation Z = h( XI, . . . , X,). To this aim, variables Xi, i = 1,. . . , n are sequentially simulated in such a manner that Z = h( xi, . . . , xi-i, Xi, . .(More)
Different probabilistic models for classification and prediction problems are anlyzed in this article studying their behaviour and capability in data classification. To show the capability of Bayesian Networks to deal with classification problems four types of Bayesian Networks are introduced, a General Bayesian Network, the Naive Bayes, a Bayesian Network(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)
In this paper, we present a method that allows a coherent assessment of probabilistic Bayesian networks over sets of nodes or variables that share a common subset. We motivate and illustrate the models with a diseases-symptoms knowledge base defined by multiple Bayesian networks. Since the parameter values for the joint probability distribution of diseases(More)