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Query DAGs: A Practical Paradigm for Implementing Belief-Network Inference
[article]
1997
arXiv
pre-print
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We describe a new paradigm for implementing inference in belief networks, which consists of two steps: (1) compiling a belief network into an arithmetic expression called a Query DAG (Q-DAG); and (2) answering ...
Interestingly enough, Q-DAGs were found to serve other purposes: simple techniques for reducing Q-DAGs tend to subsume relatively complex optimization techniques for belief-network inference, such as network-pruning ...
Special thanks to Jack Breese, Bruce D'Ambrosio and to the anonymous reviewers for their useful comments on earlier drafts of this paper. ...
arXiv:cs/9705101v1
fatcat:xww2cfqcufagvc5pszjjpiptma
Query DAGs: A Practical Paradigm for Implementing Belief Network Inference
[article]
2014
arXiv
pre-print
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We describe a new paradigm for implementing inference in belief networks, which relies on compiling a belief network into an arithmetic expression called a Query DAG (Q-DAG). ...
Each leaf node of a Q-DAG represents the answer to a network query, that is, the probability of some event of interest. ...
CONCLUSION We have introduced a new paradigm for implementing belief-network inference that is oriented towards real world, on-line applications. ...
arXiv:1408.1480v1
fatcat:fevsvpdl7zhrdlblgdxl4asrrm
Query DAGs: A Practical Paradigm for Implementing Belief-Network Inference
1997
The Journal of Artificial Intelligence Research
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We describe a new paradigm for implementing inference in belief networks, which consists of two steps: (1) compiling a belief network into an arithmetic expression called a Query DAG (Q-DAG); and (2) answering ...
Interestingly enough, Q-DAGs were found to serve other purposes: simple techniques for reducing Q-DAGs tend to subsume relatively complex optimization techniques for belief-network inference, such as network-pruning ...
Special thanks to Jack Breese, Bruce D'Ambrosio and to the anonymous reviewers for their useful comments on earlier drafts of this paper. ...
doi:10.1613/jair.330
fatcat:fjudfaigpvcejatog5d357okg4
Extending ontology queries with Bayesian network reasoning
2009
2009 International Conference on Intelligent Engineering Systems
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Inference over Bayesian Network
BQ Language
Introduction The basic computation on belief networks is the computation of the belief of every node (its conditional probability) given the evidence that ...
The property, however, that sets inference in probabilistic networks apart from other automatic reasoning paradigms is its ability to make intercausal reasoning: Getting evidence that supports solely a ...
Appendix A Bayesian Query Language: The Operational Semantics In the following is reported the operational semantics of the Bayesian Query Language. ...
doi:10.1109/ines.2009.4924756
fatcat:bmk2swmhw5chzbbqkr6ldhi43a
Causal Queries from Observational Data in Biological Systems via Bayesian Networks: An Empirical Study in Small Networks
[article]
2018
arXiv
pre-print
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We also illustrate our exploration by experiments on small simulated networks as well as on a real biological data set. ...
Biological networks are a very convenient modelling and visualisation tool to discover knowledge from modern high-throughput genomics and postgenomics data sets. ...
Lastly, we are very grateful to reviewers of this chapter for their insightful comments. ...
arXiv:1805.01608v1
fatcat:gcvsm6pkgvhmhnswo5vb22fkbe
GPU-based Commonsense Paradigms Reasoning for Real-Time Query Answering and Multimodal Analysis
[article]
2018
arXiv
pre-print
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., speech and video, for emotion and polarity detection. ...
In term of processing speed, our method shows improvements of several orders of magnitude for feature extraction compared to CPU-based counterparts. ...
In these experiments, we use 20 labels for Gowalla network and 10 labels for Enron network. The number of query vertices varies from 6 to 13. ...
arXiv:1807.08804v1
fatcat:xu5m2oh55ndahct7rtrbecjz3m
Page 499 of Mathematical Reviews Vol. , Issue 99a
[page]
1991
Mathematical Reviews
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for implementing belief-network inference (203-210); Rina Dechter, Bucket elimination: a unifying framework for probabilistic infer- ence (211-219); Rina Dechter, Topological parameters for time- space ...
upper proba- bilities (169-177); Lonnie Chrisman, Propagation of 2-monotone lower probabilities on an undirected graph (178-185); Adnan Dar- wiche and Gregory Provan, Query DAGs: a practical paradigm ...
Building Intelligent Sensor Networks with Multiagent Graphical Models
[chapter]
2008
Studies in Computational Intelligence
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However, this paradigm has a number of practical difficulties. ...
These beliefs form a distributed assessment of the current state of the domain and answer the query "what is the small set of highly probable faulty devices" in the context of sensor network. ...
doi:10.1007/978-3-540-76829-6_11
fatcat:ektide27rjdenhsi6xg6koj4ka
Spatial bayesian learning algorithms for geographic information retrieval
2005
Proceedings of the 2005 international workshop on Geographic information systems - GIS '05
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The resulting Bayesian networks were loaded into an inference engine that was used to retrieve all relevant themes given a test set of user queries. ...
The use of Bayesian inference for GIR relies on a manually created Bayesian network. The Bayesian network contains causal probability relationships between spatial themes. ...
GIR should be able to create multi theme maps from a simple user query. Bayesian Inference networks offer one such technique to retrieve multiple spatial themes given a simple user query. ...
doi:10.1145/1097064.1097080
dblp:conf/gis/WalkerPM05
fatcat:xbkvzayhp5fqnegtmuo5qy5cfe
BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks
2020
Journal of Statistical Software
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A shiny app with Cytoscape widgets provides an interactive interface for evidence absorption, queries, and visualizations. ...
Probabilistic reasoning enables a user to absorb information into a Bayesian network and make queries about how the probabilities within the network change in light of new information. ...
The BN paradigm also enables probabilistic queries within the network (Koller and Friedman 2009) . ...
doi:10.18637/jss.v094.i03
fatcat:2pgpbq3ijnfbpl762sm3pzxzd4
PAID: A Probabilistic Agent-Based Intrusion Detection system
2005
Computers & security
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Agents are capable to perform soft-evidential update, thus providing a continuous scale for intrusion detection. We propose methods for modelling errors and resolving conflicts among beliefs. ...
Finally, we have implemented a proofof-concept prototype of PAID. ª ...
The representation consists of a directed acyclic graph (DAG), prior probability tables for the nodes in the DAG that have no parents and conditional probability tables (CPTs) for the nodes in the DAG ...
doi:10.1016/j.cose.2005.06.008
fatcat:nrjvtfijd5ezdggdw556jbtqfu
Automatic belief network modeling via policy inference for SDN fault localization
2016
Journal of Internet Services and Applications
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In this paper, we propose a new approach to tackle SDN fault localization by automatically Modeling via Policy Inference (called MPI) the causality between SDN faults and their symptoms to a belief network ...
Referring to the component causality graph templates derived from SDN reference model, the implementation view of the current running network services can be modeled as a belief network. ...
a Service Belief Network for each policy (and thus its defined network service). • We implement the system and evaluate it for its accuracy and efficiency in both a simulation environment and a real network ...
doi:10.1186/s13174-016-0043-y
fatcat:ny7oyp66vjdlrehwhs4hdpi7he
Knowledge representation and diagnostic inference using Bayesian networks in the medical discourse
[article]
2019
arXiv
pre-print
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For the diagnostic inference under uncertainty Bayesian networks are investigated. The method is based on an adequate uniform representation of the necessary knowledge. ...
To this end, various inference mechanisms are introduced and subsequently evaluated within the context of a developed prototype. ...
Implementing a prototype using Infer.NET In order to test the presented methods for their practicability, a prototypical agent was implemented in C#. ...
arXiv:1909.08549v1
fatcat:plzcvm7pwbgkjazpegf2unrx5a
A multi-agent systems approach to distributed bayesian information fusion
2010
Information Fusion
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With the help of the theory of Bayesian networks and factor graphs we derive design and organization rules for modular fusion systems which implement exact belief propagation without centralized configuration ...
be distributed throughout a system of networked devices. ...
In principle, for each new sensor a new local model would be required which is not practical. ...
doi:10.1016/j.inffus.2009.09.007
fatcat:xl4hd6k5bbhenl6ro46kr6hkju
A survey in traditional information retrieval models
2008
2008 2nd IEEE International Conference on Digital Ecosystems and Technologies
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As a matter of fact, many so-called semantic search algorithms are derived from the traditional indexterm-based search models. ...
In the following, two main models based on the Bayesian model are introduced, which are the inference network model and the belief network model.
C. ...
The neutral network model provides an alternative searching paradigm. ...
doi:10.1109/dest.2008.4635214
fatcat:azzki4wfq5drbk7p2yds63a2vi
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