Transcription of Mixture Models - Carnegie Mellon University
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Mixture Models - Carnegie Mellon University
Chapter 20 Mixture Two Routes to Mixture From Factor Analysis to Mixture ModelsIn factor analysis, the origin myth is that we have a fairly small number,qof realvariables which happen to be unobserved ( latent ), and the much larger numberpof variables we do observe arise as linear combinations of these factors, plus mythology is that it s possible for us (or for Someone) tocontinuouslyadjust thelatent variables, and the distribution of observables likewise changes if the latent variables are not continuous but ordinal, or even categorical? Thenatural idea would be that each value of the latent variable would give a differentdistribution of the From Kernel Density Estimates to Mixture ModelsWe have also previously looked at kernel density estimation, where we approximatethe true distribution by sticking a small (1nweight) co
20.2 Estimating Parametric Mixture Models From intro stats., we remember that it’s generally a good idea to estimate distribu-tions using maximum likelihood, when we can. How could we do that here? Remember that the likelihood is the probability (or probability density) of ob-serving our data, as a function of the parameters.
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