Conditional probability density function estimation with sigmoidal neural networks

Previous developments in conditional density estimation have used neural nets to estimate statistics of the distribution or the marginal or joint distributions of the input-output variables. We modify the joint distribution estimating sigmoidal neural network to estimate the conditional distribution. Thus, the probability density of the output conditioned on the inputs is estimated using a neural network. We derive and implement the learning laws to train the network. We show that this network has computational advantages over a brute force ratio of joint and marginal distributions. We also compare its performance to a kernel conditional density estimator in a larger scale (higher dimensional) problem simulating more realistic conditions.