Shadow Simulated Annealing: A new algorithm for approximate Bayesian inference of Gibbs point processes

Stoica, Radu; Deaconu, Mâdâlina; Philippe, Anne; Hurtado-Gil, L.

doi:10.1016/j.spasta.2021.100505

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Multi-sensor Feature Fusion Diagnosis Of Bayes Down-turn Sho...1

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Cited by 2 publications

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“…Bayesian inference algorithm [7] is formed on the basis of network structure, but when applied to fault diagnosis, the structure will become very complex. Not only that, the complexity of the structure causes the whole reasoning process to become extremely complicated.…”

Section: Multi-sensor Feature Fusion Diagnosis Of Bayes Down-turn Sho...mentioning

confidence: 99%

The fault identification method of high energy-consuming transformer interturn short circuit considering feature coupling

Gao,

Zhang

et al. 2024

Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024)

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Section: Multi-sensor Feature Fusion Diagnosis Of Bayes Down-turn Sho...mentioning

confidence: 99%

The fault identification method of high energy-consuming transformer interturn short circuit considering feature coupling

Gao,

Zhang

et al. 2024

Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024)

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Morpho-Statistical Description of Networks Through Graph Modelling and Bayesian Inference

Laporte-Chabasse

Clausel

Stoica

et al. 2022

IEEE Trans. Netw. Sci. Eng.

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Collaboration graphs are relevant sources of information to understand behavioural tendencies of groups of individuals. The study of these collaboration graphs enables figuring out factors that may affect the efficiency and the sustainability of cooperative work. An example of such a collaboration involves researchers who develop relationships with their external counterparts to tackle scientific challenges. We propose a statistical approach that considers edge occurrence in the graph as a labelling process. Our approach combines spatial processes modelling and Exponential Random Graph Models (ERGMs) commonly used to analyse such social processes. Since the normalising constant involved in classical Markov Chain Monte Carlo approaches is not available in an analytic closed form, the inference remains challenging. To overcome this issue, we propose a Bayesian tool that relies on the recent ABC Shadow algorithm. The proposed method is illustrated on real data sets from an open archive of scholarly documents. †The equivalents in French for "Lorraine Research Laboratory in Computer Science and its Applications" http://www.loria.fr

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Shadow Simulated Annealing: A new algorithm for approximate Bayesian inference of Gibbs point processes

Cited by 2 publications

References 37 publications

The fault identification method of high energy-consuming transformer interturn short circuit considering feature coupling

The fault identification method of high energy-consuming transformer interturn short circuit considering feature coupling

Morpho-Statistical Description of Networks Through Graph Modelling and Bayesian Inference

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