Bayesian Statistics is a computational method that addresses numerical problems with probabilities. It provides the tools to evident new data that update the benefits.
Bayesian Statistics Uses
Bayesian inference is a statistical inference process in which theorem of Bayes is used to modify a hypothesis likelihood as more data or knowledge becomes available. Setting parameters and models is an essential part of Bayesian Inference. Any supervised machine learning algorithm’s objective is to estimate better the mapping function (f) for the output variable (Y) given the input data (X). The mapping function is often referred to as the target function, as it is the function to be approximated by a given supervised machine learning algorithm. The predictive errors are -
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