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Qualitative Discovery in Medical Databases
[chapter]
2000
Lecture Notes in Computer Science
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This paper empirically examines the performance of evidential inferencing with implication networks generated using a rule induction tool called KAT. ...
In particular, it attempts to show that (1) with a limited number of case samples, KAT is capable of inducing implication networks useful for making evidential inferences based on partial observations, ...
The D-S inferencing scheme may be regarded as a complex theoretical deviation from the Bayesian theory. ...
doi:10.1007/3-540-39963-1_50
fatcat:nrhtmjpawrgxze42ycutvjh4la
Unveiling the oracle: Artificial intelligence for the 21st century
2018
International Journal of Intelligent Decision Technologies
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The inability of current machines to expose biases induced by programmers and data scientists is leading towards the creation of a new religion, where machines are mystic oracles whose pronouncements have ...
In this paper we discuss the issues that can raise from biases introduced in autonomous systems, with specific care of the case of machine learning systems, and their impact on our society. ...
[33] discuss an approach to derive explanatory arguments from a Bayesian network. ...
doi:10.3233/idt-180342
fatcat:ghebewtgvzgqthvg3bhqpcyfqu
Monitoring and Diagnosis of Multistage Manufacturing Processes Using Hierarchical Bayesian Networks
2021
Procedia Manufacturing
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In addition, it can also identify the time at which the fault has occurred and the type (mean shift or variance change) and nature (step faults or slow drifts) of the change. ...
In addition, it can also identify the time at which the fault has occurred and the type (mean shift or variance change) and nature (step faults or slow drifts) of the change. ...
Acknowledgements The authors acknowledge financial support from Intel Inc. through a university research support grant. ...
doi:10.1016/j.promfg.2021.06.007
fatcat:eajyki4prfehpesn7vw6jutpee
Factored particle filtering for data fusion and situation assessment in urban environments
2005
2005 7th International Conference on Information Fusion
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Inferencing on such vector-based models exploits both causal dependencies among variables in the state vector via its dynamic Bayesian belief network representation and vector decomposition into weakly ...
The approach is demonstrated using a Marine Corps operational scenario involving a potential ambush on city streets. ...
Enabling Technologies Dynamic Bayesian Networks (DBNs) (Murphy, 2000; Ghahramani, 2001 ) are simply Bayesian networks (Pearl 1988) for modeling time series data. ...
doi:10.1109/icif.2005.1591961
fatcat:asj64xowqbhuhm7pxtjpkceaoy
Inference of Gene Regulatory Networks Using Bayesian Nonparametric Regression and Topology Information
2017
Computational and Mathematical Methods in Medicine
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Here we develop a Bayesian group lasso with spike and slab priors to perform gene selection and estimation for nonparametric models. ...
for the lack of reliable expression data. ...
Therefore, our method is an effective way of inferencing gene regulatory network from the time-series data. level at time and ( , ) is the set of all the parent nodes of gene at time . ...
doi:10.1155/2017/8307530
pmid:28133490
pmcid:PMC5241943
fatcat:hu4yfmdy2vexdam53375t3bkpm
Using the Bayesian Network to Map Large-Scale Cropping Intensity by Fusing Multi-Source Data
2019
Remote Sensing
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Second, the regional, intra-class variations detected in time-series remote sensing data across vast areas represent environment-related clusters for each cropping intensity level. ...
We then proposed a Bayesian network model fusing prior knowledge (BNPK) to address the issue of intra-class variations when mapping CII over large areas. ...
Method The proposed method for mapping cropping intensity from the time-series MODIS data consists of two components: (1) the definition of the cropping intensity index, and (2) the Bayesian network modeling ...
doi:10.3390/rs11020168
fatcat:klpkv3mbqvhkhg6kms54opiqmm
Distributed probabilistic inferencing in sensor networks using variational approximation
2008
Journal of Parallel and Distributed Computing
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This paper considers the problem of distributed inferencing in a sensor network. ...
It particularly explores the probabilistic inferencing problem in the context of a distributed Boltzmann machine-based framework for monitoring the network. ...
Eamonn Keogh for making available useful time series data sets at http://www.cs.ucr.edu/ ∼ eamonn/time series data/. ...
doi:10.1016/j.jpdc.2007.07.011
fatcat:ajajzhmplfdzfnxf54amrqpabq
A new uncertainty measure for belief networks with applications to optimal evidential inferencing
2001
IEEE Transactions on Knowledge and Data Engineering
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both the efficiency and the quality of the D-S belief network-based evidential inferencing. ...
We demonstrate, with Monte Carlo simulation, the implementation and the effectiveness of the proposed dynamical observer in solving the problem of evidential inferencing with optimal evidence node selection ...
are validated using the values from the same data samples. ...
doi:10.1109/69.929899
fatcat:et63jac7qfarjjgb3sxpjdlybm
Risk Assessment of Public Safety and Security Mobile Service
2015
2015 10th International Conference on Availability, Reliability and Security
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a temporal Bayesian network from time series data; and (iii) construction of a causal model using temporal data and multiple iterations in Taboo learning. ...
network from this data; and secondly, to the usage of Bayesian networks to document the probabilistic nature of CSP business processes. ...
doi:10.1109/ares.2015.65
dblp:conf/IEEEares/PeltolaK15
fatcat:iqzzjbyqrzbmfanhry2rj742pi
Secure Configuration of Intrusion Detection Sensors for Changing Enterprise Systems
[chapter]
2012
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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An example would be compromising a web server, then achieve a series of intermediary steps (such as compromising a developer's box thanks to a vulnerable PHP module and connecting to a FTP server with ...
As shown in the experiments, the framework reduces the number of false positives that it would otherwise report if it were only considering alerts from a single detector and the reconfiguration of sensors ...
There are several benefits of using Bayesian networks. ...
doi:10.1007/978-3-642-31909-9_3
fatcat:q55ukrshkrf5pkc7s5l22hmfua
Neural Sampling Machine with Stochastic Synapse allows Brain-like Learning and Inference
[article]
2021
arXiv
pre-print
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Many real-world mission-critical applications require continual online learning from noisy data and real-time decision making with a defined confidence level. ...
We also showcase the Bayesian inferencing capability introduced by the stochastic synapse during inference mode, thus accounting for uncertainty in data. ...
Bayesian Inferencing and Capturing Uncertainty in Data Next, we showcase the ability of our simulated hardware-NSM to perform Bayesian inferencing and produce classification confidence. ...
arXiv:2102.10477v1
fatcat:hbc6f7zfmbekziylkg2iajibt4
Learning and Reasoning in Complex Coalition Information Environments: A Critical Analysis
2018
2018 21st International Conference on Information Fusion (FUSION)
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., accurate and deep understanding of a situation derived from uncertain and often sparse data and collective foresight-i.e., the ability to predict what will happen in the future. ...
When it comes to complex scenarios, the need for a distributed CSU naturally emerges, as a single monolithic approach not only is unfeasible: it is also undesirable. ...
Similarly, multiple-entity Bayesian networks uses a series of rule-based fragments to compose Bayesian network for reasoning [42] . ...
doi:10.23919/icif.2018.8455458
dblp:conf/fusion/CeruttiAXHBBCKK18
fatcat:ktslx27imbe5jbqcjduzgcq35q
Decision support in power systems based on load forecasting models and influence analysis of climatic and socio-economic factors
2006
Wavelet Applications in Industrial Processing IV
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relationships from the data. ...
The same data of the annual consumption series supplied as input for the regression analysis, as described in section 2, were also submitted to the neural network and the Kalman filter. ...
Data selection and preparation The database used to generate the Bayesian networks was provided by the power supplier of the State of Pará, the climatic data by the National Institute of Spatial Researches ...
doi:10.1117/12.686433
fatcat:zwri2jl4fbddznbeslnpsucxy4
Comparative Benchmarking of Causal Discovery Techniques
[article]
2017
arXiv
pre-print
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For (b) and (c) we train causal Bayesian networks with structures as predicted by each causal discovery technique to carry out counterfactual or standard predictive inference. ...
Experiments show that structural accuracy of a technique does not necessarily correlate with higher accuracy of inferencing tasks. ...
A causal Bayesian network (CBN) is a Bayesian network with each edge representing cause and effect relationship from parent to child. ...
arXiv:1708.06246v2
fatcat:2h6vhkd5pfcfpei3o6t7rjtw3y
An Approach to Tacit Knowledge Classification in a Manufacturing Company
2019
Tehnički Vjesnik
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The Bayesian Network was used for this purpose, which was modelled on the defined relationships between representing of tacit knowledge. ...
is, the use of algorithms based on clustering them, for calculations; (3) interpretation of results. ...
Figure 1 1 The Bayesian Network at rest; made in GeNie Based on the data from Tab. 2, three selected simulations of network operation were performed; these comprised the clustering of the knowledge studied ...
doi:10.17559/tv-20180308155129
fatcat:wokli7cpfffkdmhgoqdupqfjza
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