
Chih Cheng Liang
1.1K posts

Chih Cheng Liang
@ChihChengLiang
Independent researcher


The Fisher Information Metric is a fundamental concept linking probability, statistics, and geometry. For a parametric model p(x;θ), it is defined as I(θ) = E[(∂/∂θ log p(x;θ))²], measuring how sensitive a distribution is to changes in θ. Geometrically, it induces a Riemannian metric on the space of probability distributions, forming the basis of information geometry. In statistics, it determines the Cramér–Rao lower bound, setting a limit on estimator variance. In machine learning, it appears in natural gradient descent, where updates are scaled by the inverse Fisher matrix, leading to more efficient optimization in deep models. In real life, it underpins signal processing, neuroscience (neural coding efficiency), and experimental design, where maximizing Fisher information ensures more informative data collection and better inference.


> they are hard core open source 99% of the miladys i have seen have never shipped a real open source project in their life. we dont need this LARP culture for cypherpunk values





Decentralization clicks for institutions and tradfi people when reframed as counterparty risk. - Good point made by (I think) @dannyryan at devconnect












Dee Hock on the founding of Visa in his work "Chaordic Organization". Visa was perhaps the first successful case of a permissionless platform with decentralized governance and ownership. Many lessons to be learned here half a century later for crypto networks.










