FACTORIAL HIDDEN MARKOV MODELS ESTIMATION DEVICE, METHOD, AND PROGRAM

摘要

An approximate computation unit computes an approximate of a determinant of a Hessian matrix relating to a parameter of an observation model represented as a linear combination of parameters determined by each layer 1 latent variable of factorial hidden Markov models. A variational probability computation unit computes a variational probability of a latent variable using the approximate of the determinant. A latent state removal unit removes a latent state based on a variational distribution. A parameter optimization unit optimizes the parameter for a criterion value that is defined as a lower bound of an approximate obtained by Laplace-approximating a marginal log-likelihood function with respect to an estimator for a complete variable, and computes the criterion value. A convergence determination unit determines whether or not the criterion value has converged.