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- Andrés Cano, Serafín Moral
- IPMU
- 1994

Dierent uncertainty propagation algorithms in graph-ical structures can be viewed as a particular case of propagation in a joint tree, which can be obtained from dierent triangulations of the original graph. The complexity of the resulting propagation algorithms depends on the size of the resulting triangulated graph. The problem of obtaining an optimum… (More)

- Serafín Moral, Andrés Cano
- Annals of Mathematics and Artificial Intelligence
- 2002

This paper investigates the concept of strong conditional independence for sets of probability measures. Couso, Moral and Walley [7] have studied different possible definitions for unconditional independence in imprecise probabilities. Two of them were considered as more relevant: epistemic independence and strong independence. In this paper, we show that… (More)

- Andrés Cano, Serafín Moral
- Int. J. Approx. Reasoning
- 2002

This paper presents an approximate algorithm to obtain a posteriori intervals of probability, when available information is also given with intervals. The algorithm uses probability trees as a means of representing and computing with the convex sets of probabilities associated to the intervals.

- Andrés Cano, Manuel Gómez-Olmedo, Serafín Moral, Joaquín Abellán
- Int. J. Approx. Reasoning
- 2007

This paper proposes two new algorithms for inference in credal networks. These algorithms enable probability intervals to be obtained for the states of a given query variable. The first algorithm is approximate and uses the hill-climbing technique in the Shenoy–Shafer architecture to propagate in join trees; the second is exact and is a modification of… (More)

The propagation of probabilities in credal networks when probabilities are estimated with a global imprecise Dirichlet model is an important open problem. Only Zaffalon [21] has proposed an algorithm for the Naive classifier. The main difficulty is that, in general, computing upper and lower probability intervals implies the resolution of an optimization of… (More)

- Andrés Cano, Serafín Moral, Antonio Salmerón
- Int. J. Intell. Syst.
- 2000

This paper presents non-random algorithms for approximate computation in Bayesian networks. They are based on the use of probability trees to represent probability potentials, using the Kullback-Leibler cross entropy as a measure of the error of the approximation. Different alternatives are presented and tested in several experiments with difficult… (More)

- Andrés Cano, Serafín Moral
- ISIPTA
- 1999

This paper reviews algorithms for local computation with imprecise probabilities. These algorithms try to solve problems of inference (calculation of conditional or unconditional probabilities) in cases in which there are a large number of variables. There are two main types depending on the nature of assumed independence relationships in each case. In both… (More)

- Andrés Cano, José E. Cano, Susana Moral, Andr Es Cano, Jos, E. Cano
- 1993

Propagation of convex sets of probabilities in dependence structures poses additional computational problems on top of the propagation of a single probability distribution. If we want to calculate the maximum and minimum of the conditional probabilities for a concrete event, we have to search on the space of the possible probability distributions. In a… (More)

- Andrés Cano, Serafín Moral, Antonio Salmerón
- Networks
- 2002

In this paper, we investigate the application of the ideas behind Lazy propagation to the Penniless propagation scheme. Probabilistic potentials attached to the messages and the nodes of the join tree are represented in a factorized way as a product of (approximate) probability trees, and the combination operations are postponed until they are compulsory… (More)

- Andrés Cano, Manuel Gómez-Olmedo, Serafín Moral
- Int. J. Approx. Reasoning
- 2011

The present paper introduces a new kind of representation for the potentials in a Bayesian network: binary probability trees. They enable the representation of context-specific independences in more detail than probability trees. This enhanced capability leads to more efficient inference algorithms for some types of Bayesian networks. This paper explains… (More)