Transcription of Gaussian mixture models and the EM algorithm - People
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Gaussian mixture models and the EM algorithm - People
Gaussian mixture models and the EM algorithmRamesh Sridharan These notes give a short introduction to Gaussian mixture models (GMMs) and theExpectation-Maximization (EM) algorithm , first for the specific case of GMMs, and thenmore generally. These notes assume you re familiar with basic probability and basic you re interested in the full derivation (Section 3), some familiarity with entropy and KLdivergence is useful but not strictly notation here is borrowed fromIntroduction to Probabilityby Bertsekas & Tsitsiklis:random variables are represented with capital letters, values they take are represented withlowercase letters,pXrepresents a probability distribution for random variableX, andpX(x)represents the probability of valuex(according topX). We ll also use the shorthand notationXn1to represent the sequenceX1,X2.
The answer is no. Intuitively, we can see this by looking at the fundamental property of the normal distribution: it’s highest near the center, and quickly drops o as you get farther away. But, the distribution of a randomly chosen book is bimodal: the center of
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