54 citations · 150 across the 7 of their papers we have counts for
8 papers
Bayesian Models of Data Streams with Hierarchical Power Priors
Andres Masegosa, Thomas D. Nielsen, Helge Langseth +3
Making inferences from data streams is a pervasive problem in many modern data analysis applications. But it requires to address the problem of continuous model updating and adapt…
AMIDST: a Java Toolbox for Scalable Probabilistic Machine Learning
Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López +5
The AMIDST Toolbox is a software for scalable probabilistic machine learning with a spe- cial focus on (massive) streaming data. The toolbox supports a flexible modeling language b…
Lazy Propagation in Junction Trees
Anders L. Madsen, Finn Verner Jensen
The efficiency of algorithms using secondary structures for probabilistic inference in Bayesian networks can be improved by exploiting independence relations induced by evidence an…
Lazy Evaluation of Symmetric Bayesian Decision Problems
Anders L. Madsen, Finn Verner Jensen
Solving symmetric Bayesian decision problems is a computationally intensive task to perform regardless of the algorithm used. In this paper we propose a method for improving the ef…
Solving Influence Diagrams using HUGIN, Shafer-Shenoy and Lazy Propagation
Anders L. Madsen, Dennis Nilsson
In this paper we compare three different architectures for the evaluation of influence diagrams: HUGIN, Shafer-Shenoy, and Lazy Evaluation architecture. The computational complexit…
An Empirical Evaluation of Possible Variations of Lazy Propagation
Anders L. Madsen
As real-world Bayesian networks continue to grow larger and more complex, it is important to investigate the possibilities for improving the performance of existing algorithms of p…