EM maximizes G ( 0 ) via block-coordinate ascent on a lower bound F ( q , 0 ) using an auxiliary distribution over the latent variables q ( z | x , y ) ( Neal and Hinton 1998 ) :
a new view of the em algorithm that justifies incremental sparse and other variants
Radford M Neal Geoffrey E Hinton
Because of the latent alignment variables z , the log-likelihood function for the HMM model is not concave , and the model is fit using the Expectation Maximization ( EM ) algorithm ( Dempster , Laird , and Rubin 1977 ) . EM maximizes G ( 0 ) via block-coordinate ascent on a lower bound F ( q , 0 ) using an auxiliary distribution over the latent variables q ( z | x , y ) ( Neal and Hinton 1998 ) : step uses qt +1 to fill the values of alignments z and estimate parameters This step is particularly easy for HMMs , where simply involves normalizing ( expected ) counts .
EM maximizes G ( 0 ) via block-coordinate ascent on a lower bound F ( q , 0 ) using an auxiliary distribution over the latent variables q ( z | x , y ) ( @@CITATION ) :