BNN

INTRODUCTION - Bayesian neural networks (BNNs) are a type of neural network that uses Bayesian statistics to estimate the uncertainty of their predictions. This is i n contrast to traditional n eural networks, which only provide point estimates, i.e., a single prediction for each input. BNNs work by placing a prior distribution over the parameters of the neural network. This prior distribution represents the model's beliefs about the parameters before seeing any training data. Once the model is trained on the data, the posterior distribution over the parameters is updated. The posterior distribution represents the model's beliefs about the parameters after seeing the training data. The posterior distribution over the parameters can be used to estimate the uncertainty of the model's predictions. For example, the predictive mean can be used as the point estimate, and the predictive variance can be used as a measure of uncertainty. BNNs have a number of advantages over tradi...