Probabilistic Networks (probabilistic + network)

Distribution by Scientific Domains


Selected Abstracts


PREPROCESSING RULES FOR TRIANGULATION OF PROBABILISTIC NETWORKS,

COMPUTATIONAL INTELLIGENCE, Issue 3 2005
Hans L. Bodlaender
Currently, the most efficient algorithm for inference with a probabilistic network builds upon a triangulation of a network's graph. In this paper, we show that pre-processing can help in finding good triangulations for probabilistic networks, that is, triangulations with a maximum clique size as small as possible. We provide a set of rules for stepwise reducing a graph, without losing optimality. This reduction allows us to solve the triangulation problem on a smaller graph. From the smaller graph's triangulation, a triangulation of the original graph is obtained by reversing the reduction steps. Our experimental results show that the graphs of some well-known real-life probabilistic networks can be triangulated optimally just by preprocessing; for other networks, huge reductions in their graph's size are obtained. [source]


Modularizing inference in large causal probabilistic networks

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 2 2003
Kristian G. Olesen
This article describes a number of implementation aspects of modular inference in large medical expert systems based on causal probabilistic networks. Examples are provided from the neuromuscular diagnosting system the muscle and nerve inference network (MUNIN). The inference procedure is outlined and the principal data structure underlying the inference procedure are described. A condensed summary of selected technical details of the inference procedure in causal probabilistic networks (CPNs) is provided. This is required for understanding the implemented modularization of the inference. The modularization of the inference implies a need for transfer of information between modules, which is realized by establishing communication channels between modules. Modules are also used to perform inference by conditioning, a method that reduces storage requirements to a manageable size and thereby prepares the way for MUNINs migration to common PCs. © 2003 Wiley Periodicals, Inc. [source]


Census and monitoring based on individually identifiable vocalizations: the role of neural networks

ANIMAL CONSERVATION, Issue 2 2002
Andrew M. R. Terry
Vocal individuality is widely suggested as a method for identifying individuals within a population. But few studies have explored its performance in real or simulated conservation situations. Here we simulated the use of vocal individuality to monitor the calling corncrake (Crex crex), a secretive and endangered land rail. Our data set contained 600 calls from 30 individuals and was used to simulate a population of corncrakes being counted and monitored. We tested three different neural network models for their ability to discriminate between and to identify individuals. Neural networks are non-linear classification tools widely applied to both biological and non-biological identification tasks. Backpropagation and probabilistic neural networks were used to simulate the reidentification of members of a known population (monitoring) and a Kohonen network was used to simulate the counting of a population of unknown size (census). We found that both backpropagation and probabilistic networks identified all individuals correctly all the time, irrespective of sample size. Kohonen networks were more variable in performance but estimated population size to within one individual of the actual size. Our results indicate that neural networks can be used effectively together with recordings of vocalizations in census and monitoring tasks. [source]