Dear Nidhal,

It is an interesting question. To answer the question properly, the following papers may be referred:

1. Pan, G., Fu, L., Thakali, L., Muresan, M., & Yu, M. (2018). An Improved Deep Belief Network Model for Road Safety Analyses (No. 18-00835).

2. Abid, F., & Hamami, L. (2018). A survey of neural network based automated systems for human chromosome classification. Artificial Intelligence Review, 49(1), 41-56.

3. Zhu, Y., & Zabaras, N. (2018). Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification. arXiv preprint arXiv:1801.06879.

Thanks,

Sobhan

Dear Nidhal,

It is an interesting question. To answer the question properly, the following papers may be referred:

1. Pan, G., Fu, L., Thakali, L., Muresan, M., & Yu, M. (2018). An Improved Deep Belief Network Model for Road Safety Analyses (No. 18-00835).

2. Abid, F., & Hamami, L. (2018). A survey of neural network based automated systems for human chromosome classification. Artificial Intelligence Review, 49(1), 41-56.

3. Zhu, Y., & Zabaras, N. (2018). Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification. arXiv preprint arXiv:1801.06879.

Thanks,

Sobhan

Hi Furat,

You probably refer to "Understanding Bayes: Evidence vs. Conclusions" by A. Etz readable from https://alexanderetz.com/tag/conclusions/.

Etz's post focuses on Dempster-Shafer's theory of evidence. Follow:

• Section 4 from Florea et al., "An Unified Approach to the Fusion of Imperfect Data ", 2002 - http://lrts.gel.ulaval.ca/publications/uploadPDF/publication_9.pdf
• Yang et al., " A Study on Generalising Bayesian Inference to Evidential Reasoning ", 2014 - https://php.portals.mbs.ac.uk/Portals/49/docs/YangXuBelief14.pdf

Among other applications of evidential reasoning, let us cite data regression and clustering taking data uncertainty into account. Check out:

• T. Denoeux, "A k-Nearest Neighbor Classification Rule Based on Dempster-Shafer Theory", 1995 - http://sci2s.ugr.es/keel/pdf/algorithm/articulo/1995-IEEE_TSMC-Denoeux.pdf
• Petit-Renaud et al., "Nonparametric regression analysis of uncertain and imprecise data using belief functions", 2003 - http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.63.9920&rep=rep1&type=pdf

Finally, Bayesian Deep Learning is reviewed by following:

• Wang et al., "Towards Bayesian Deep Learning: A Survey", 2016 - https://arxiv.org/pdf/1604.01662.pdf
• Wang et al., "Towards Bayesian Deep Learning: A Framework and Some Existing Methods", 2016 - http://www.wanghao.in/paper/TKDE16_BDL.pdf

Regards

Thanks a lot dears. i should check the papers you mentioned in your answers

Best Regards