ABSTRACT
Belief networks are developed for addressing issues in classical networks with layered structures. This addresses the problem of the delayed training and getting stuck at minima, which happens when we select poor performance parameters. This requires a huge amount of datasets, which is a big problem in the current scenario. Belief networks are probability models built with stochastic calculated layers with latent parameters. These layers have hidden layers that contain binary values, called features or hidden values. The layers on top are not structured, and there is an accurate connection between all layers. The layers at the bottom get the data from the connected layers, and the parameters of classes have to change according to the datasets and according to the case being used. There appears to be an ideal layer-by-layer technique in training the top-down, creating parameters that describe whether all parameters within one layer rely on the variables inside the level below.. In the real world, this format of layered structure helps to build different applications with fast execution and accurate results of training. This chapter gives an insight into how belief networks are formed and their use cases. Furthermore, we discuss the current world applications of belief networks, including key aspects and drawbacks.
