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Bayesian Networks for Network Intrusion Detection
[chapter]
2010
Bayesian Network
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as graphical probabilistic models for multivariate analysis. ...
Moreover, approximate evidence propagation methods can also be applied, in order to improve inference and adaptation time of response. ...
It contains recent developments in the field and illustrates, on a sample of applications, the power of Bayesian networks in dealing the modeling of complex systems. ...
doi:10.5772/10069
fatcat:fy4w2wfmf5cv7gvu7u6supho7u
Bayesian Neural Networks: An Introduction and Survey
[chapter]
2020
Lecture notes in mathematics
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2020 pre-print arXiv:2006.12024v1 fatcat:xt3rvct43bck7nck255d4utray
Different approximate inference methods are compared, and used to highlight where future research can improve on current methods. ...
This article introduces Bayesian Neural Networks (BNNs) and the seminal research regarding their implementation. ...
This review and these experiments highlight the capabilities of Bayesian analysis to address common challenges seen in the machine learning community. ...
doi:10.1007/978-3-030-42553-1_3
fatcat:rzkjcf6h3vcarkqstsocfjqpri
Probabilistic Models with Deep Neural Networks
[article]
2019
arXiv
pre-print
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However, developments in variational inference, a general form of approximate probabilistic inference originated in statistical physics, are allowing probabilistic modeling to overcome these restrictions ...
: (i) Approximate probabilistic inference is now possible over a broad class of probabilistic models containing a large number of parameters, and (ii) scalable inference methods based on stochastic gradient ...
Acknowledgements This research has been partly funded by the Spanish Ministry of Science, Innovation and Universities, through projects TIN2015-74368-JIN, TIN2016-77902-C3-3-P and by ERDF funds. ...
arXiv:1908.03442v3
fatcat:2ep7jwaq2bgvdocuxzk3nrflhi
Probabilistic Models with Deep Neural Networks
2021
Entropy
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However, developments in variational inference, a general form of approximate probabilistic inference that originated in statistical physics, have enabled probabilistic modeling to overcome these limitations ...
: (i) Approximate probabilistic inference is now possible over a broad class of probabilistic models containing a large number of parameters, and (ii) scalable inference methods based on stochastic gradient ...
Thirdly, with a combined focus on inference and modeling, we may balance the results of performing approximate inference in "exact models" and performing exact inference in "approximate models" (with the ...
doi:10.3390/e23010117
pmid:33477544
pmcid:PMC7831091
fatcat:wzitmmruvjbehgdie2wgtk7dtq
Passive Diagnosis for Wireless Sensor Networks
2010
IEEE/ACM Transactions on Networking
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Instead, we introduce a probabilistic inference model that encodes internal dependencies among different network elements for online diagnosis of an operational sensor network system. ...
Existing sensor debugging tools like sympathy or EmStar rely heavily on an add-in protocol that generates and reports a large amount of status information from individual sensor nodes, introducing network ...
PAD employs a probabilistic model to infer the statuses of unobservable network elements and reveal the root faults in the network. ...
doi:10.1109/tnet.2009.2037497
fatcat:xb2zn4vfyvh7nidg3hm3m4ndze
Active Learning of Spin Network Models
[article]
2019
arXiv
pre-print
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Our active learning framework could be powerful in the analysis of complex networks as well as in the rational design of experiments. ...
We apply the framework to the inference of spin network models and find that designed perturbations can reduce the sampling complexity by 10^6-fold across a variety of network architectures. ...
To demonstrate our framework, we constrain ourselves to a specific and canonical class of network models, the spin networks as probabilistic graphical models. ...
arXiv:1903.10474v3
fatcat:vbbztyeo4vhqhechyfb7ncdmo4
Probabilistic Latent Tensor Factorization Model for Link Pattern Prediction in Multi-relational Networks
[article]
2012
arXiv
pre-print
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This paper aims at the problem of link pattern prediction in collections of objects connected by multiple relation types, where each type may play a distinct role. ...
of the proposed probabilistic model to avoid overfitting for solving the LPP problem. ...
Acknowledgments We would like to thank Kurt T.Miller for providing the Kinship and Countries datasets. ...
arXiv:1204.2588v1
fatcat:5utozeoaabetlbhjntdnd6zp4a
Sparse Relational Topic Models for Document Networks
[chapter]
2013
Lecture Notes in Computer Science
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assumptions for approximate inference. ...
Learning latent representations is playing a pivotal role in machine learning and many application areas. ...
In contrast, the probabilistic RTM often makes mean-field assumptions for approximate inference. ...
doi:10.1007/978-3-642-40988-2_43
fatcat:h3xnk5l7abb3vi22lfbhylv7z4
Fault Localization for Self-Managing Based on Bayesian Network
베이지안 네트워크 기반에 자가관리를 위한 결함 지역화
2008
The KIPS Transactions PartB
베이지안 네트워크 기반에 자가관리를 위한 결함 지역화
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The selected node ordering lists will be used in network modeling, and hence improving learning efficiency. ...
The experimental application of system performance analysis by using the proposed approach and various estimations on efficiency and accuracy show that the availability of the proposed approach in performance ...
Generally, probabilistic
inferences must be done in an environment of uncertainty
where the domain information is incomplete and incoming
data is uncertain or partially unavailable. ...
doi:10.3745/kipstb.2008.15-b.2.137
fatcat:4vfeorqh5verdkbfciy5qcww7m
Interference effects in quantum belief networks
2014
Applied Soft Computing
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2014 pre-print arXiv:1409.8470v1 fatcat:z3flip7xwfh25e4bz4db5lg4i4
Probabilistic graphical models such as Bayesian Networks are one of the most powerful structures known by the Computer Science community for deriving probabilistic inferences. ...
The results obtained revealed that the quantum like Bayesian Network can affect drastically the probabilistic inferences, specially when the levels of uncertainty of the network are very high (no pieces ...
Generally speaking, a Bayesian Network is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph. ...
doi:10.1016/j.asoc.2014.09.008
fatcat:z5xq44i3obbtrkfc2wwflpzkfu
Nonparametric Bayesian modeling of complex networks: an introduction
2013
IEEE Signal Processing Magazine
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Modeling structure in complex networks using Bayesian non-parametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. ...
infinite limit of a finite parametric model, inferring the model parameters by Markov chain Monte Carlo, and checking the model's fit and predictive performance. ...
AcknowlEdgMEnt This work is funded in part by the Lundbeck Foundation. ...
doi:10.1109/msp.2012.2235191
fatcat:psujjh4ozrcytbogtue5qtt5ju
A Bayesian network modeling approach for cross media analysis
2011
Signal processing. Image communication
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More specifically, our contribution is on proposing a modeling approach for Bayesian Networks that defines this conceptual space and allows evidence originating from the domain knowledge, the application ...
In this work we implement a cross media analysis scheme that takes advantage of both visual and textual information for detecting high-level concepts. ...
Acknowledgment This work was funded by the X-Media project (www.x-media-project.org) sponsored by the European Commission as part of the Information Society Technologies (IST) programme under EC grant ...
doi:10.1016/j.image.2011.01.004
fatcat:5mlr54qminazxls6f4umnuj3xm
Affiliation recommendation using auxiliary networks
2010
Proceedings of the fourth ACM conference on Recommender systems - RecSys '10
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Social network analysis has attracted increasing attention in recent years. ...
factors to model users and communities. ...
ACKNOWLEDGEMENTS We thank Prateek Jain and Berkant Savas for helpful discussions. We also thank Alan Mislove [9] for providing access to Orkut and Youtube datasets. ...
doi:10.1145/1864708.1864731
dblp:conf/recsys/VasukiNLD10
fatcat:fartvctggfdb5ng74bysqseg7q
Visualizing and understanding Sum-Product Networks
2018
Machine Learning
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Sum-Product Networks (SPNs) are recently introduced deep tractable probabilistic models by which several kinds of inference queries can be answered exactly and in a tractable time. ...
We do this with a threefold aim: first we want to get a better understanding of the inner workings of SPNs; secondly, we seek additional ways to evaluate one SPN model and compare it against other probabilistic ...
While for classical density estimators such as Probabilistic Graphical Models (PGMs) (Koller and Friedman 2009), like Markov Networks (MNs) and Bayesian Networks (BNs), performing exact inference is ...
doi:10.1007/s10994-018-5760-y
fatcat:y6dmj37cmncuvgzgkl4khkpk3y
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
[article]
2020
arXiv
pre-print
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We show that, on real datasets, our approach can outperform state-of-the-art Bayesian and non-Bayesian graph neural network algorithms on the task of semi-supervised classification in the absence of graph ...
We formulate a joint probabilistic model that considers a prior distribution over graphs along with a GCN-based likelihood and develop a stochastic variational inference algorithm to estimate the graph ...
This work was conducted in partnership with the Defence Science and Technology Group, through the Next Generation Technologies Program. ...
arXiv:1906.01852v5
fatcat:uxkkrn2klfbllap7srrsl46jbq
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