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Bayesian Networks for Network Intrusion Detection [chapter]

Pablo Garcia, Igor Santos
2010 Bayesian Network  
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]

Ethan Goan, Clinton Fookes
2020 Lecture notes in mathematics  
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]

Andrés R. Masegosa, Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón
2019 arXiv   pre-print
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

Andrés R. Masegosa, Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón
2021 Entropy  
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

Yunhao Liu, Kebin Liu, Mo Li
2010 IEEE/ACM Transactions on Networking  
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]

Jialong Jiang, David A. Sivak, Matt Thomson
2019 arXiv   pre-print
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]

Sheng Gao and Ludovic Denoyer and Patrick Gallinari
2012 arXiv   pre-print
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]

Aonan Zhang, Jun Zhu, Bo Zhang
2013 Lecture Notes in Computer Science  
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

Shun-Shan Piao, Jeong-Min Park, Eun-Seok Lee
2008 The KIPS Transactions PartB  
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

Catarina Moreira, Andreas Wichert
2014 Applied Soft Computing  
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

Mikkel N. Schmidt, Morten Morup
2013 IEEE Signal Processing Magazine  
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

Christina Lakka, Spiros Nikolopoulos, Christos Varytimidis, Ioannis Kompatsiaris
2011 Signal processing. Image communication  
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

Vishvas Vasuki, Nagarajan Natarajan, Zhengdong Lu, Inderjit S. Dhillon
2010 Proceedings of the fourth ACM conference on Recommender systems - RecSys '10  
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

Antonio Vergari, Nicola Di Mauro, Floriana Esposito
2018 Machine Learning  
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]

Pantelis Elinas, Edwin V. Bonilla, Louis Tiao
2020 arXiv   pre-print
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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