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Qualitative Discovery in Medical Databases [chapter]

David A. Maluf, Jiming Liu
2000 Lecture Notes in Computer Science  
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

Federico Cerutti, Alessia Grassi, Mauro Vallati
2018 International Journal of Intelligent Decision Technologies  
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

Partha Protim Mondal, Placid Matthew Ferreira, Shiv Gopal Kapoor, Patrick N Bless
2021 Procedia Manufacturing  
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

S. Das, D. Lawless, B. Ng, A. Pfeffer
2005 2005 7th International Conference on Information Fusion  
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

Yue Fan, Xiao Wang, Qinke Peng
2017 Computational and Mathematical Methods in Medicine  
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

Jianbin Tao, Wenbin Wu, Meng Xu
2019 Remote Sensing  
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

Sourav Mukherjee, Hillol Kargupta
2008 Journal of Parallel and Distributed Computing  
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 ∼ 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

Jiming Liu, D.A. Maluf, M.C. Desmarais
2001 IEEE Transactions on Knowledge and Data Engineering  
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

Matti J. Peltola, Pekka Kekolahti
2015 2015 10th International Conference on Availability, Reliability and Security  
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]

Gaspar Modelo-Howard, Jevin Sweval, Saurabh Bagchi
2012 Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering  
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]

Sourav Dutta, Georgios Detorakis, Abhishek Khanna, Benjamin Grisafe, Emre Neftci, Suman Datta
2021 arXiv   pre-print
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

Federico Cerutti, Moustafa Alzantot, Tianwei Xing, Daniel Harborne, Jonathan Z. Bakdash, Dave Braines, Supriyo Chakraborty, Lance Kaplan, Angelika Kimmig, Alun Preece, Ramya Raghavendra, Murat Sensoy (+1 others)
2018 2018 21st International Conference on Information Fusion (FUSION)  
., 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

Cláudio A. Rocha, Ádamo L. de Santana, Carlos R. Francês, Ubiratan Bezerra, Armando Tupiassú, Vanja Gato, Liviane Rego, João Costa, Frédéric Truchetet, Olivier Laligant
2006 Wavelet Applications in Industrial Processing IV  
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]

Karamjit Singh, Garima Gupta, Vartika Tewari, Gautam Shroff
2017 arXiv   pre-print
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  
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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