After having read the related chapters of Jaynes' book I was fairly amazed by the Principle of Maximum Entropy, a powerful method for choosing prior distributions. However it immediately raised a large number of questions.
I have recently read two quite intriguing (and very well-written) papers by Jos Uffink on this matter:
Can the maximum entropy principle be explained as a consistency requirement?
The constraint rule of the maximum entropy principle
I was wondering what you think about the principle of maximum entropy and its justifications.
Hello, I am Kris,
I study Mathematics and Computer Science at Oxford, I am interested in learning about Bayesian statistics/machine learning and its principles (Cox's theorem, Principle of MaxEnt) and tend to do things (overly) rigorously.
From my very limited experience, it appears that lesswrong applies these principles to real life, which is interesting as well, but at the moment I am more focused on Jaynes' "robot model".
I really like Jaynes' book, however it has come to my attention that some parts are outdated/unrigorous and I'm hoping that this forum will tell me what the state of the art is.
Looking forward to becoming part of the community :)