Bayesian Neural Networks with TensorFlow Probability

Machine learning models are usually developed from data as deterministic machines that map input to output using a point estimate of parameter weights calculated by maximum-likelihood methods. However, there is a lot of statistical fluke going on in the background. For instance, a dataset itself is a finite random set of points of arbitrary size from a unknown distribution superimposed by additive noise, and for such a particular collection of points, different models (i.e. Hence, there is some uncertainty about the parameters and predictions being made. Bayesian statistics provides a framework to deal with the so-called aleoteric and epistemic uncertainty, and with the release of TensorFlow Probability, probabilistic modeling has been made a lot easier, as I shall demonstrate with this post.