Bayesian Neural net with non probibalistic Data?
It is possible to predict a single value from a Bayesian Neural Network. Given a set of input data, conduct the forward pass to generate the resulting probability distribution. Then convert that probability distribution to a single specific value in one of the following common ways:


Sample - Take a random sample. That random sample will automatically be weighted by the posterior probability distribution. This type of sampling is similar to Thompson sampling.
Use a measure of central tendency. Given that posterior probability distribution, calculate the most useful measure of central tendency (e.g., mean, median, or mode).