Creating neural networks which infer whole probability densities or certainties for their predictions, rather than point estimates.
In Bayesian terms this is about estimating a posterior distribution, and in frequentist terms… uh… What is a pithy frequentist phrasing?
Anyway, AFAICT this usually boils down to doing variational inference, in which case the neural netwrok is a big approximate PDGM. Apparently you can also do simulation-based inference here, somehow using gradients? Must look into that.
Yarin Gal’s PhD Thesis summarises a lot of stuff here: Uncertainty in Deep Learning.
Blei Lab’s software tool: Edward (source) Tensorflow indeed comes with a contributed Bayesian library called BayesFlow (Which is not the same as the cytometry library of the same name) which by contrast has documentation so perfunctory that I can’t imagine it not being easier to reimplement it.
Christopher Bonnett: Mixture Density Networks with Edward, Keras and TensorFlow.
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- Gal, Y. (2016) Uncertainty in Deep Learning (phdthesis). . University of Cambridge
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- Tran, D., Kucukelbir, A., Dieng, A. B., Rudolph, M., Liang, D., & Blei, D. M.(2016) Edward: A library for probabilistic modeling, inference, and criticism. arXiv:1610.09787 [Cs, Stat].
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