 The advantage of this type of model is that it does not suffer from the so-called "label bias" problem of MEMM's, and thus may make more accurate predictions.The disadvantage is that training can be slower than for MEMM's.Yet another variant is the "factorial hidden Markov model", which allows for a single observation to be conditioned on the corresponding hidden variables of a set of formula_37 independent Markov chains, rather than a single Markov chain.