Bayesian Intervals
Bayesian intervals, often called credible intervals, are a statistical concept used in Bayesian inference to quantify uncertainty about an unknown parameter. They represent a range of values within which the parameter is believed to lie with a specified probability, based on prior knowledge and observed data. Unlike frequentist confidence intervals, Bayesian intervals provide a direct probabilistic interpretation of the parameter's uncertainty.
Developers should learn Bayesian intervals when working on data science, machine learning, or statistical modeling projects that require uncertainty quantification, such as A/B testing, predictive analytics, or risk assessment. They are particularly useful in fields like healthcare, finance, and engineering, where incorporating prior information and providing interpretable probability statements is crucial for decision-making under uncertainty.