Live briefing
Everything about EM algorithm and GMM model
Numerical method
In statistics, EM algorithm handles latent variables, while GMM is the Gaussian mixture model. Wikipedia →
🧠Start here: a 6-step reading path
- Read the overview: EM algorithm and GMM modelA 5-minute orientation
- Skim a recent paper: Planar ECT Image Reconstruction Based on Solving the Bayesian Model by Combining Fast Iterative Adaptive Shrinkage-Thresholding Algorithm and GMMInfluential work since 2022
- Read the landmark paper: User-level malicious behavior analysis model based on the NMF-GMM algorithm and ensemble strategyCited 9 times
- Go sideways: Expectation–maximization algorithmA closely related topic
- Go sideways: Mixture modelA closely related topic
đź“– Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
User-level malicious behavior analysis model based on the NMF-GMM algorithm and ensemble strategy
An Algorithm of Unsupervised Posture Clustering and Modeling Based on GMM and EM Estimation
Optimizing Electric Vehicle Clustering through Improved MVABC-GMM Algorithm Based on Gaussian Mixture Model and Monte Carlo Prediction
🆕 Influential since 2022
Planar ECT Image Reconstruction Based on Solving the Bayesian Model by Combining Fast Iterative Adaptive Shrinkage-Thresholding Algorithm and GMM
Smoke Detection Algorithm on Digital Image Sequences Using Gaussian Mixture Model (GMM) Segmentation Method and Region Growing for Fire Detection System
Retraction Note: Algorithm research on the influence of financing structure and cash holding on enterprise innovation based on system GMM model function theory
đź“š Books worth reading
Open Library didn't respond; try again in a moment.
đź”— Related topics
- Expectation–maximization algorithm In statistics, an expectation–maximization (EM) algorithm is an iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates…
- Mixture model In statistics, a mixture model is a probabilistic model for representing the presence of subpopulations within an overall population, without requiring…
- Variational Bayesian methods Variational Bayesian methods are a family of techniques for approximating intractable integrals arising in Bayesian inference and machine learning. They…
- GHK algorithm The GHK algorithm (Geweke, Hajivassiliou and Keane) is an importance sampling method for simulating choice probabilities in the multivariate probit model…
- Glicko rating system The Glicko rating system and Glicko-2 rating system are methods of assessing a player's strength in zero-sum two-player games. The Glicko rating system…
- Truncated normal distribution In probability and statistics, the truncated normal distribution is the probability distribution derived from that of a normally distributed random variable…
- Thurstonian model A Thurstonian model is a stochastic transitivity model with latent variables for describing the mapping of some continuous scale onto discrete, possibly…
- Folded normal distribution The folded normal distribution is a probability distribution related to the normal distribution. Given a normally distributed random variable X with mean…