Allohvk·Oct 27The Road Less Travelled: Venturing beyond the NormalIntuition Behind Fat Tails, Student’s t, Mixture Models, and GMMsA response icon4A response icon4
Isuru Udayangani Hewapathirana·Sep 9Gaussian Mixture Models, SimplifiedYou already know the Gaussian distribution: one bell curve, described by a mean and a spread. But real data rarely comes from a single bell…
Oxotall·Jul 21Mixture Density NetworksImagine a self-driving car approaching an obstacle in the middle of the road. Human drivers in the logs went around it on the left half the…
Pranav Agrawal·Jul 18Gaussian Mixture Models: Guide to Soft ClusteringImagine you’re handed a fish that could be a big salmon or a small tuna. Ask K-means and it will answer instantly, with total confidence…
InCode AppliedbyGustavo R Santos·Jul 16Gaussian Mixture Models (GMM) Explained: A Simple GuideLearn how Gaussian Mixture Models work in machine learning, how they use soft clustering, and why they go beyond simple normal…A response icon1A response icon1
Mr. Vivek Kumar·Jul 15GMM in Distribution Based Clustering in DetailGaussian Mixture Model (GMM) in Distribution/Model Based Clustering
Jimmy C·Jun 16Volatility Regime Detection: From K-Means to Hidden Markov ModelsQuant Padawan Journey: Post 2, A Hedgeye-Inspired Approach to Clustering Market Environments
Chwmwei·Mar 16Manual Derivation Series: EM Algorithm and Gaussian Mixture ModelEM (Expectation-Maximization) algorithm’s core idea is very simple, consisting of two steps: Expectation-Step and Maximization-Step. The…
InTowards AIbyMariangela Bonghi·Feb 233 Algorithms and 4 Configurations for Customer SegmentationPart 2 — Identifying optimal parameters for K-Means, GMM, and Linkage methods
