Explore >> Select a destination
|
You are here 
|
www.datagenetics.com | ||
| | | | |
aurimas.eu
|
|
| | | | | a.k.a. why you should (not ?) use uninformative priors in Bayesian A/B testing. | |
| | | | |
emiruz.com
|
|
| | | | | ||
| | | | |
www.quantstart.com
|
|
| | | | | Bayesian Statistics: A Beginner's Guide | |
| | | | |
francisbach.com
|
|
| | | [AI summary] This text discusses the scaling laws of optimization in machine learning, focusing on asymptotic expansions for both strongly convex and non-strongly convex cases. It covers the derivation of performance bounds using techniques like Laplace's method and the behavior of random minimizers. The text also explains the 'weird' behavior observed in certain plots, where non-strongly convex bounds become tight under specific conditions. The analysis connects theoretical results to practical considerations in optimization algorithms. | ||
