An optimal neural-network model for learning posterior probability functions from observations

This paper presents the further results of the authors' former work [1] in which a neural-network method was proposed for sequential detection with similar performance as the optimal sequential probability ratio tests (SPRT) [2]. The analytical results presented in the paper show that the neural network is an optimal model for learning the posterior conditional probability functions, with arbitrarily small error, from the sequential observation data under the condition in which the prior probability density functions about the observation sources are not provided by the observation environment.