Research Topics:

Data Analysis

Content Type:

Glossary

Bayesian Statistics Definition

Statistics which incorporate prior knowledge and accumulated experience into probability calculations.

Bayesian statistics offer a better understanding of uncertainty than its more classical-based statistical treatments. With its basis in Bayesian probably, Bayesian statistics incorporates prior beliefs with new evidence collected to obtain a posterior distribution. A posterior probability is the revised or updated probability of an event occurring after being taken into consideration. Thus, we can update our beliefs or knowledge about a particular event or parameter as new data becomes available. That makes the Bayesian approach more flexible and adaptive. Conversely, classical statistics treats parameters as fixed and data as random. The Bayesian approach allows for comprehensive understanding of uncertainty and can be useful in complex and small-scale situations.

Who relies on Bayesian statistics?

Bayesian statistics can be applied in such fields as scientific research, engineering, medical studies, economics, machine learning and artificial intelligence. Bayesian methods are used to make informed decisions, to model complex systems and to handle uncertainty effectively.

Why should I care about Bayesian statistics?

Bayesian statistics allow prior knowledge or beliefs to be incorporated into analysis, which is useful when dealing with limited data. This can lead to more accurate inferences. What’s more, Bayesian methods call for beliefs to be updated as new data emerges, thus allowing for continuous growth in the understanding of problems and other issues. The bottom line is Bayesian statistics can provide a powerful tool to handle uncertainty and make well-informed choices.