A Bayesian Neural Network is a type of artificial neural network that incorporates Bayesian inference in its learning and decision-making processes. It combines the flexibility and adaptability of neural networks with the probabilistic framework of Bayesian inference.

At its core, a Bayesian Neural Network is composed of interconnected nodes, or neurons, that mimic the structure and function of the human brain. Each neuron receives input signals, processes them, and produces an output signal. These signals, also known as activations, are transmitted through weighted connections between neurons.

The key difference between a traditional neural network and a Bayesian Neural Network lies in the treatment of uncertainty. Traditional neural networks typically assign fixed weights to connections, which remain constant throughout the learning process. In contrast, Bayesian Neural Networks incorporate uncertainty by assigning probability distributions to the weights, allowing for more flexible and robust learning.

The Bayesian inference aspect of a Bayesian Neural Network allows it to make predictions and decisions by taking into account prior knowledge and updating it with new evidence. This iterative process helps the network make more accurate predictions, especially in situations with limited data or high levels of noise.

Bayesian Neural Networks have gained popularity in various fields, including finance, healthcare, and computer vision. Due to their ability to handle uncertainty and make robust predictions, they are particularly useful in situations where decision-making under uncertainty is critical.

In conclusion, a Bayesian Neural Network is an artificial neural network that integrates Bayesian inference into its learning and decision-making processes. It combines the flexibility of neural networks with the probabilistic framework of Bayesian inference to handle uncertainty and make robust predictions. Its applications span across different industries, making it a valuable tool in various domains.