I understand the benefit of modelling aleatoric uncertainty, e.g. to be able to deal with heteroscedastic noise. However, why do we need to model epistemic uncertainty? The best prediction after all, lies in the middle of the final distribution. If you sample from the distribution, you will lose accuracy. So is uncertainty only useful for certain applications to determine different behaviour based on high uncertainty? For example: If uncertainty is high, drive slower?
The other presentation Eric mentions is that of Nicole Carlson: Turning PyMC3 into scikit learn ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-zGRnirbHWJ8.html
Thank you so much! This has helped me so much with my project and really helped to understand both deep learning and bayesian deep learning much better. I really appreciate it!