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The Fisher Information

The machine learning consultancy: https://truetheta.io Join my email list to get educational and useful articles (and nothing else!): https://mailchi.mp/truetheta/true-the... Want to work together? See here: https://truetheta.io/about/#want-to-w... Article on the topic: https://truetheta.io/concepts/machine... The Fisher Information quantifies how well an observation of a random variable locates a parameter value. It's an essential tool for measure parameter uncertainty, a problem that repeats itself throughout machine learning and statistics. In this video, I explain the Fisher Information rigorously and visually, starting in the one dimensional case and ending in the general case. SOCIAL MEDIA LinkedIn :   / dj-rich-90b91753   Twitter :   / duanejrich   Enjoy learning this way? Want me to make more videos? Consider supporting me on Patreon:   / mutualinformation   Sources and Learning More [1] provides a complete and deep explanation of the Fisher Information. It's captures the abstract/general perspective while making the idea concrete with examples. As is typically the case, the wikipedia article [2] was helpful. Also, section 8.2.2 of [3] explains the use of a theorem on the asymptotic normality of the MLE via the Fisher Information, which I didn't cover here, but certainly informed how I think it connects to parameter uncertainty. [1] Ly A., Marsman M., Verhagen J., Grasman R., Wagermarkers E.J., (2017), A Tutorial on the Fisher Information, Department of Psychological Methods, University of Amsterdam, The Netherlands [2] Fisher information, Wikipedia, https://en.wikipedia.org/wiki/Fisher_... [3] Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. New York: Springer.