Unification of MAP Estimation and Marginal Inference in Recurrent Neural Networks.

Abstract

Numerous experimental data show that human brain can represent probability distributions and perform Bayesian inference. However, it remains unclear how the brain implements probabilistic inference in the form of neural circuits. Several models have been proposed that aim at explaining how the network of neurons carry out maximum a posterior inference (MAP) estimation and marginal inference, but they are all task specific in that they treat MAP estimation and marginal inference separately. In this brief, we propose that human brain could implement MAP estimation and marginal inference in the same network of neurons. We illustrate our result in hidden Markov models and prove that a recurrent neural network (RNN) implementation of belief propagation can be tuned to perform approximate Bayesian inference (to provide posterior or conditional distribution over the latent causes of observations) or identify the MAP or peak of the joint distribution. The key tuning parameter is a temperature parameter that controls the precision of probability distributions that are optimized. Theoretical analyses and experimental results demonstrate that RNNs can carry out near-optimal MAP estimation and marginal inference.