Variational maximum A posteriori by annealed mean field analysis
This paper proposes a novel probabilistic variational method with deterministic annealing for the maximum a posteriori (MAP) estimation of complex stochastic systems. Since the MAP estimation involves global optimization, in general, it is very difficult to achieve. Therefore, most probabilistic inference algorithms are only able to achieve either the exact or the approximate posterior distributions. Our method constrains the mean field variational distribution to be multivariate Gaussian. Then, a deterministic annealing scheme is nicely incorporated into the mean field fix-point iterations to obtain the optimal MAP estimate. This is based on the observation that when the covariance of the variational Gaussian distribution approaches to zero, the infimum point of the Kullback-Leibler (KL) divergence between the variational Gaussian and the real posterior will be the same as the supreme point of the real posterior. Although global optimality may not be guaranteed, our extensive synthetic and real experiments demonstrate the effectiveness and efficiency of the proposed method.
deterministic annealing mean field variational analysis maximum a posteriori estimation graphical model markov network