![]() Table of Contentsįor the discrete case, consider \(N\) different possibilities, e.g. ![]() Here are some common probability distributions and how to derive them from the principle of maximum entropy. So even as a way to understand the world, maximum entropy is a very useful and deep tool. Moreover, many physical patterns found in nature tend toward maximum entropy probability distributions. Implementing an algorithm which by Nicole Chantzi Intuition Medium Physics The Principle of Maximum Entropy Implementing an algorithm that maximizes. But from an information-theoretic perspective, these will be the least biased prior distributions (we maximize our ignorance) so subsequent experiments a la Bayes’ theorem will maximize the information gained. Nearly all real-world applications of reinforcement learning. For me that alone is worth the cost of entry. Maximum Entropy RL (Provably) Solves Some Robust RL Problems. The cool thing is that these maximum entropy distributions are quite common, so this is a neat way of re-deriving many of the distibutions we encounter day-to-day. ![]() After de ning entropy and computing it in some examples, we will describe this principle and see how it provides a natural conceptual role for many standard probability distributions (normal, exponential, Laplace, Bernoulli). You take a few knowns or constraints, and then maximize information entropy subject to these conditions and voila! you have a unique probability distribution. It is called the principle of maximum entropy. I think the method of maximum entropy to obtain probability distributions is so cool. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |