EM_Presentation
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This document discusses Gaussian mixture density estimation and algorithms like Expectation-Maximization (EM) for estimating distributions. EM is an iterative algorithm that starts with an initial approximation of the distribution and refines it through E and M steps to better fit sample data. Other related algorithms mentioned are Split-EM (SMEM) which splits distributions to improve fitting, and Merge which combines split distributions.
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EM_Presentation
- 1. Gaussian Mixture Density Estimation INNL BNT Andrey Lange
- 3. ,),;();( 1 ∑ = Σ= Q k kkk xgwxf µθξ The example of Gaussian mixture distribution ,2 R∈ξ .5=Q
- 5. The estimating distribution (3D) Initial approximationThe estimating distribution The result of ЕМ algorithm The result of ЕМ algorithm (3D)
- 6. EM SMEM random sample and initial approximation The result of ЕМ procedure Split procedure The result of Split The result of Merge Algorithms EM and SMEM random sample generating
- 7. EM SME M