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Extreme inaccuracies in Gaussian Bayesian networks
2008
Journal of Multivariate Analysis
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This analysis is performed to find the effect of extreme uncertainty about the initial parameters of the model in a Gaussian Bayesian network and about extreme values of evidence. ...
To evaluate the impact of model inaccuracies over the network's output, after the evidence propagation, in a Gaussian Bayesian network, a sensitivity measure is introduced. ...
In Section 1 definitions of Bayesian networks and Gaussian Bayesian networks are introduced and the process of evidence propagation in Gaussian Bayesian networks is reviewed. ...
doi:10.1016/j.jmva.2008.02.027
fatcat:3z65kqwerrhnldigqhuakgz22u
The effect of block parameter perturbations in Gaussian Bayesian networks: Sensitivity and robustness
2013
Information Sciences
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In this work we study the effects of model inaccuracies on the description of a Gaussian Bayesian network with a set of variables of interest and a set of evidential variables. ...
We describe two methods for analyzing the sensitivity and robustness of a Gaussian Bayesian network on this basis. ...
Literature about sensitivity analysis in Gaussian Bayesian networks (GBNs) is not extensive. ...
doi:10.1016/j.ins.2012.08.004
fatcat:65f23fswyncdnlzhjxenezngzu
Impact of precision of Bayesian network parameters on accuracy of medical diagnostic systems
2013
Artificial Intelligence in Medicine
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While most knowledge engineers believe that the quality of results obtained by means of Bayesian networks is not too sensitive to imprecision in probabilities, this remains a conjecture with only modest ...
We summarize the results of several previously presented experiments involving Hepar II model, in which we manipulated the quality of the model's numerical parameters and checked the impact of these manipulations ...
All Bayesian network models in this paper were created and tested using SMILE, an inference engine, and GeNIe, a development environment for reasoning in graphical probabilistic models, both developed ...
doi:10.1016/j.artmed.2013.01.004
pmid:23466438
pmcid:PMC4486041
fatcat:gsuducv6uzhzvf2lrk52bxu364
Statistical Learning Methods Applicable to Genome-Wide Association Studies on Unbalanced Case-Control Disease Data
2021
Genes
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This review paves the way for better analysis and understanding of the unbalanced case-control disease data in GWAS. ...
In this article, we review multiple statistical approaches that have been developed to overcome the inaccuracy caused by the unbalanced case-control ratio, with the advantages and limitations of each approach ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/genes12050736
pmid:34068248
pmcid:PMC8153154
fatcat:yfhzgv6jinhd3ppmhieo6ivbv4
Gravitational wave population inference with deep flow-based generative network
[article]
2020
arXiv
pre-print
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We combine hierarchical Bayesian modeling with a flow-based deep generative network, in order to demonstrate that one can efficiently constraint numerical gravitational wave (GW) population models at a ...
In this study, we train a network to emulate a phenomenological model with 6 observables and 4 hyper-parameters, use it to infer the properties of a simulated catalogue and compare the results to the phenomenological ...
More specifically, we use a normalizing flow network to emulate the likelihood in a Hierarchical Bayesian Analysis (HBA) framework. ...
arXiv:2002.09491v1
fatcat:gdoqbheesrggjpbzy2mkxoqay4
Bayesian filtering for indoor localization and tracking in wireless sensor networks
2012
EURASIP Journal on Wireless Communications and Networking
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Additionally, statistical analysis of the real data is provided, reinforcing the idea that in this kind of ranging measurements, the Gaussian noise assumption does not hold. ...
In this article, we investigate experimentally the suitability of several Bayesian filtering techniques for the problem of tracking a moving device by a set of wireless sensor nodes in indoor environments ...
Acknowledgements The authors would like to thank the anonymous reviewers for their constructive comments and suggestions, which have significantly improved the quality of this work. ...
doi:10.1186/1687-1499-2012-21
fatcat:wcu3s6axy5dtlbu2htm7uprem4
Sensitivity-Informed Bayesian Inference for Home PLC Network Models with Unknown Parameters
2021
Energies
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The sensitivity-informed Bayesian inference computes Sobol indices for each network parameter and applies TMCMC to calibrate the most sensitive parameters for a given network topology. ...
A network topology with over 50 parameters is calibrated using global sensitivity analysis and transitional Markov Chain Monte Carlo (TMCMC). ...
The network transfer function is solved by first finding the main path between the transmitter and receiver, and then recursively combining matrices into the main path. ...
doi:10.3390/en14092402
doaj:176a116be7114ad5b796b786e9f5f340
fatcat:odqudlb4zfba3buyd5hgkceexy
Bayesian networks in biomedicine and health-care
2004
Artificial Intelligence in Medicine
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We are thankful to all of them for their devotion to achieving success for both the workshop and this special issue. ...
Acknowledgements In addition to the editors, the following people were involved in both this workshop and the special issue, and in particular reviewed submitted papers: K. ...
Such an analysis serves to provide insight in the robustness of the output of the network to possible inaccuracies in the underlying probability distribution [7, 15] . ...
doi:10.1016/j.artmed.2003.11.001
pmid:15081072
fatcat:r52evw5rofdozp5tzs24sft46m
Metabolica: A statistical research tool for analyzing metabolic networks
2010
Computer Methods and Programs in Biomedicine
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In metabolic models, the stoichiometry of the system, commonly completed with bounds on some of the variables, is used as the constraint in the search of a meaningful solution. ...
Bayesian FBA treats the unknowns as random variables and provides estimates of their probability density functions. ...
, leading to problems that without additional information do not have a unique solution, or have solutions that are unfeasible or extremely sensitive to perturbations. ...
doi:10.1016/j.cmpb.2009.07.007
pmid:19748150
pmcid:PMC2814918
fatcat:344u2po6xzgatosmm6t7g2bgee
Search for the Gravitational-wave Background from Cosmic Strings with the Parkes Pulsar Timing Array Second Data Release
[article]
2022
arXiv
pre-print
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in the string-theory-inspired models. ...
burst from cosmic string cusps in the PPTA DR2. ...
A cosmic string network consists of both long (or "infinite") strings that are longer than the horizon size and loops formed from smaller strings. ...
arXiv:2205.07194v1
fatcat:pknexssw4be7zkxcekdfk4rtdq
Statistical analysis of nonlinear dynamical systems using differential geometric sampling methods
2011
Interface Focus
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Along the way, we highlight the deep link between the sensitivity analysis of such dynamic system models and the underlying Riemannian geometry of the induced posterior probability distributions. ...
In this paper, the Bayesian approach to statistical inference is adopted and we examine the significant challenges that arise when performing inference over nonlinear ordinary differential equation models ...
Some parameter values may be perturbed by large amounts and yet still have very little effect on the model output; in contrast, other parameters may be extremely sensitive in terms of their effect on model ...
doi:10.1098/rsfs.2011.0051
pmid:23226584
pmcid:PMC3262297
fatcat:z6ssnigvr5bonbwz3zoff7lxqm
Integrative Bayesian Analysis of Brain Functional Networks Incorporating Anatomical Knowledge
[article]
2018
arXiv
pre-print
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We propose a hierarchical Bayesian Gaussian graphical modeling approach that estimates the functional networks via sparse precision matrices whose degree of edge-specific shrinkage is informed by anatomical ...
Despite the clear merits, major challenges persist in integrative analyses including an incomplete understanding of the structure-function relationship and inaccuracies in mapping anatomical structures ...
Structurally informed Bayesian Gaussian graphical model Bayesian GGM approaches have been successfully used for estimating brain networks (see Mumford and Ramsey (2014) for a review). ...
arXiv:1803.00513v1
fatcat:3ibiji5xlrappbxzmljap7226i
Application of Bayesian Approach to Reduce the Uncertainty in Expert Judgments by Using a Posteriori Mean Function
2021
Mathematics
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Since the paper uses a continuous case of the Bayesian formula, perceived as a continuous approximation of experts' evaluations, this is not only the novelty of this work, but also a new result in the ...
theory of the Bayesian method and its application. ...
Conflicts of Interest: The author declares no conflict of interest. ...
doi:10.3390/math9192455
fatcat:ml3cf4sewfabbefrhawlugb47m
Estimating contaminant source in chemical industry park using UAV-based monitoring platform, artificial neural network and atmospheric dispersion simulation
2017
RSC Advances
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In terms of traditional methods including Bayesian inference and optimization, it is clear that the inaccuracy in their results is mainly brought about by the errors in the input parameters. ...
In terms of wind speed, the sensitivity analysis result shows that the ACR remains unchanged at 100% when the deviation of wind speed noise varies from 0 to 3 m s À1 . ...
doi:10.1039/c7ra05637k
fatcat:wo475x34ozaqvb5xaccoiiunle
A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems
2018
IEEE Transactions on Smart Grid
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2018 pre-print arXiv:1809.00057v1 fatcat:wzchzpg2azhuvcdgijhlvgqy3eOther Versions
This paper presents a review of the literature on State Estimation (SE) in power systems. ...
The paper discusses a few critical topics of DSSE, including mathematical problem formulation, application of pseudo-measurements, metering instrument placement, network topology issues, impacts of renewable ...
It is shown that the Bayesian approach has specifically better performance in presence of non-Gaussian uncertainty. ...
doi:10.1109/tsg.2018.2870600
fatcat:3h555tzqvvbfpfkogemsfg2jzy
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