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Recently, a general method for analyzing the statistical accuracy of the EM algorithm has been developed and applied to some simple latent variable models [Balakrishnan et al.
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[author] Dasgupta, SanjoyS. and Schulman, LeonardL. (2007). A probabilistic analysis of EM for mixtures of separated, spherical Gaussians. J. Mach. Learn. Res. 8 203–226. 2320668 \endbibitem
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[author] McLachlan, Geoffrey J.G. J. and Krishnan, ThriyambakamT. (2008). The EM algorithm and extensions, second ed. Wiley Series in Probability and Statistics. Wiley-Interscience [John Wiley & Sons], Hoboken, NJ. 10.1002/9780470191613 2392878 \endbibitem
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[author] Cook, John D.J. D. (2009). Upper and lower bounds for the normal distribution function. http://www.johndcook.com/normalbounds.pdf . \endbibitem
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[author] Pereira, José R.J. R., Marques, Leyne A.L. A. and da Costa, José M.J. M. (2012). An empirical comparison of EM initialization methods and model choice criteria for mixtures of skew-normal distributions. Rev. Colombiana Estadíst. 35 457–478. 3075156 \endbibitem
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[author] Anandkumar, AnimashreeA., Ge, RongR., Hsu, DanielD., Kakade, Sham M.S. M. and Telgarsky, MatusM. (2014). Tensor decompositions for learning latent variable models. J. Mach. Learn. Res. 15 2773–2832. 3270750 \endbibitem
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[author] Balakrishnan, SivaramanS., Wainwright, Martin J.M. J. and Yu, BinB. (2016). Statistical guarantees for the EM algorithm: From population to sample-based analysis. Annals of Statistics, to appear. \endbibitem
2016
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