  Learning the parameters of graphical models using the maximum likelihood
estimation is generally hard which requires an approximation. Maximum composite
likelihood estimations are statistical approximations of the maximum likelihood
estimation which are higher-order generalizations of the maximum
pseudo-likelihood estimation. In this paper, we propose a composite likelihood
method and investigate its property. Furthermore, we apply our composite
likelihood method to restricted Boltzmann machines.
