Transcription of IEOR E4570: Machine Learning for OR&FE Spring 2015 …
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IEOR E4570: Machine Learning for OR&FE Spring 2015 …
ieor e4570 : Machine Learning for OR&FESpring 2015c 2015 by Martin HaughThe EM AlgorithmThe EM algorithm is used for obtaining maximum likelihood estimates of parameters when some of the data ismissing. More generally, however, the EM algorithm can also be applied when there islatent, unobserved,data which was never intended to be observed in the first place. In that case, we simply assume that the latentdata is missing and proceed to apply the EM algorithm. The EM algorithm has many applications throughoutstatistics. It is often used for example, in Machine Learning and data mining applications, and in Bayesianstatistics where it is often used to obtain the mode of the posterior marginal distributions of The Classical EM AlgorithmWe begin by assuming that the complete data-set consists ofZ= (X,Y)but that onlyXis observed.
The EM Algorithm The EM algorithm is used for obtaining maximum likelihood estimates of parameters when some of the data is missing. More generally, however, the EM algorithm can also be applied when there is latent, i.e. unobserved, ... Letting g( j old) denote the right-hand-side of (3), we therefore have l( ;X) g( j old) for all with ...
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