Bayesian inference is a statistical method based on Bayes 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 Bayesian inference is used widely. From the philosophical perspective and decision theory, Bayesian inference is strongly correlated to predictive probability. This theory, however, is more formally known as the Bayesian probability.