Cora B. Perez-Ariza | Universidad de Granada (original) (raw)
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Papers by Cora B. Perez-Ariza
leo.ugr.es
In this paper we present an agricultural case study for learning Bayesian networks (BNs), namely ... more In this paper we present an agricultural case study for learning Bayesian networks (BNs), namely prediction of coffee rust. Wide-spread in all major production areas, coffee rust causes premature defoliation, weakening the plant and reducing subsequent yield. It is typically controlled by use of chemical fungicides which must be applied before symptoms of infection are observed. Improved prediction would reduce the use of fungicides, producing healthier quality product and decreasing both economic costs and environmental impact.
ual.es
We present an efficient procedure for factorising probabilistic potentials represented as probabi... more We present an efficient procedure for factorising probabilistic potentials represented as probability trees. This new procedure is able to detect some regularities that cannot be captured by existing methods. In cases where an exact decomposition is not achievable, we propose a heuristic way to carry out approximate factorisations guided by a parameter called factorisation degree, which is fast to compute. We show how this parameter can be used to control the tradeoff between complexity and accuracy in approximate inference algorithms for Bayesian networks.
Proceedings of the Fifth …, Jan 1, 2010
A recursive probability tree (RPT) is an incipient data structure for representing the distributi... more A recursive probability tree (RPT) is an incipient data structure for representing the distributions in a probabilistic graphical model. RPTs capture most of the types of independencies found in a probability distribution. The explicit representation of these features using RPTs simplifies computations during inference. This paper describes a learning algorithm that builds a RPT from a probability distribution. Experiments prove that this algorithm generates a good approximation of the original distribution, thus making available all the advantages provided by RPTs.
We present a fast potential decomposition algorithm that seeks for proportionality in a probabili... more We present a fast potential decomposition algorithm that seeks for proportionality in a probability tree. We give a measure that determines the accuracy of a decomposition in case that exact factorization is not possible. This measure can be used to decide the variable with respect to which a tree should be factorized in order to obtain the most accurate decomposed model.
This paper proposes a new data structure for representing potentials. Recursive probability trees... more This paper proposes a new data structure for representing potentials. Recursive probability trees are a generalization of probability trees. Both structures are able to represent context-specific independencies, but the new one is also able to hold a potential in a factorized way. This new structure can represent some kinds of potentials in a more efficient way than probability trees, and it can be the case that only recursive trees are able to represent certain factorizations. Basic operations for inference in Bayesian networks can be directly performed upon recursive probability trees.
leo.ugr.es
In this paper we present an agricultural case study for learning Bayesian networks (BNs), namely ... more In this paper we present an agricultural case study for learning Bayesian networks (BNs), namely prediction of coffee rust. Wide-spread in all major production areas, coffee rust causes premature defoliation, weakening the plant and reducing subsequent yield. It is typically controlled by use of chemical fungicides which must be applied before symptoms of infection are observed. Improved prediction would reduce the use of fungicides, producing healthier quality product and decreasing both economic costs and environmental impact.
ual.es
We present an efficient procedure for factorising probabilistic potentials represented as probabi... more We present an efficient procedure for factorising probabilistic potentials represented as probability trees. This new procedure is able to detect some regularities that cannot be captured by existing methods. In cases where an exact decomposition is not achievable, we propose a heuristic way to carry out approximate factorisations guided by a parameter called factorisation degree, which is fast to compute. We show how this parameter can be used to control the tradeoff between complexity and accuracy in approximate inference algorithms for Bayesian networks.
Proceedings of the Fifth …, Jan 1, 2010
A recursive probability tree (RPT) is an incipient data structure for representing the distributi... more A recursive probability tree (RPT) is an incipient data structure for representing the distributions in a probabilistic graphical model. RPTs capture most of the types of independencies found in a probability distribution. The explicit representation of these features using RPTs simplifies computations during inference. This paper describes a learning algorithm that builds a RPT from a probability distribution. Experiments prove that this algorithm generates a good approximation of the original distribution, thus making available all the advantages provided by RPTs.
We present a fast potential decomposition algorithm that seeks for proportionality in a probabili... more We present a fast potential decomposition algorithm that seeks for proportionality in a probability tree. We give a measure that determines the accuracy of a decomposition in case that exact factorization is not possible. This measure can be used to decide the variable with respect to which a tree should be factorized in order to obtain the most accurate decomposed model.
This paper proposes a new data structure for representing potentials. Recursive probability trees... more This paper proposes a new data structure for representing potentials. Recursive probability trees are a generalization of probability trees. Both structures are able to represent context-specific independencies, but the new one is also able to hold a potential in a factorized way. This new structure can represent some kinds of potentials in a more efficient way than probability trees, and it can be the case that only recursive trees are able to represent certain factorizations. Basic operations for inference in Bayesian networks can be directly performed upon recursive probability trees.