There's been enough progress in approximate Bayesian methods that many things can be done thousands of times faster than back then, as well. The reputation of Bayesian methods as being slow is undeserved nowadays.
Can someone point me to any examples where Bayesian neural networks are successfully used for any practical applications? Like where they are better than regular non-Bayesian NNs? By better I mean better accuracy.
Bayesian network is a synonym for directed graphical model.
Any time you see graphical models, they’re usually BNs. Undirected graphical models are very closely related too (all directed models can be represented as undirected models, but not all undirected models can represent directed models), but they’re usually not referred to as BNs.
They’re used all over the place. One school of causal inference is heavily steeped in BNs/DAGs. This shouldn’t be surprising because the creator of BNs, Judea Pearl, is heavily involved in causal inference now.
Not NN, just simple BN. A risk assessment application. More specifically, calculation of financial risk of climate change-related risks for the mining sector. Link:https://www.mdpi.com/2412-3811/4/3/38
Interesting example, but it's funny how parts of the abstract read like an intro probability class:
> The framework estimates the climate change risks in economic terms by modeling the main activities that a mining company performs, in a probabilistic model, using Bayes’ theorem. The model permits incorporating inherent uncertainty via fuzzy logic and is implemented in two versatile ways: as a discrete Bayesian network or as a conditional linear Gaussian network. This innovative quantitative methodology produces probabilistic outcomes in monetary values estimated either as percentage of annual loss revenue or net loss/gains value.
