Rafael Cabañas

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This paper proposes the use of binary trees in order to represent and evaluate asymmetric decision problems with Influence Diagrams (IDs). Constraint rules are used to represent the asymmetries between the variables of the ID. These rules and the potentials involved in IDs will be represented using binary trees. The application of these rules can reduce the(More)
Influence diagrams are probabilistic graphical models used to represent and solve decision problems under uncertainty. Sharp numerical values are required to quantify probabilities and utilities. Yet, real models are based on data streams provided by partially reliable sensors or experts. We propose an interval-valued quantification of these parameters to(More)
The AMIDST Toolbox an open source Java 8 library for scalable learning of probabilistic graphical models (PGMs) based on both batch and streaming data. An important application domain with streaming data characteristics is the banking sector, where we may want to monitor individual customers (based on their financial situation and behavior) as well as the(More)
Finding an optimal elimination ordering is a NP-hard problem of crucial importance for the efficiency of the Influence Diagrams evaluation. Some of the traditional methods for determining the elimination ordering use heuristics that consider that potentials are represented as tables. However, if potentials are represented using binary trees traditional(More)
Influence Diagrams are a tool used to represent and solve decision problems under uncertainty. One of the most efficient exact methods used to evaluate Influence Diagrams is Lazy Evaluation. This paper proposes the use of trees for representing potentials involved in an Influence Diagram in order to obtain an approximate Lazy Evaluation of decision(More)
The AMIDST Toolbox is a software for scalable probabilistic machine learning with a special focus on (massive) streaming data. The toolbox supports a flexible modeling language based on probabilistic graphical models with latent variables and temporal dependencies. The specified models can be learnt from large data sets using parallel or distributed(More)