Bayesian inference

In this section, we will briefly discuss Bayesian inference (BI) and its underlying theory. Readers will be familiar with this concept from the theoretical and viewpoints.

An overview of Bayesian inference

Bayesian inference is a statistical method based on theorem. It is used to update the probability of a hypothesis (as a strong statistical proof) so that statistical models can repeatedly update towards more accurate learning. In other words, all types of uncertainty are revealed in terms of statistical probability in the Bayesian inference approach. This is an important technique in theoretical as well as mathematical statistics. We will discuss the Bayes theorem broadly in a later section.

Furthermore, Bayesian updating is predominantly foremost in the incremental learning and dynamic analysis of the sequence of the dataset. For example time series analysis, genome sequencing in biomedical data analytics, science, engineering, philosophy, and law are some example where...