Bayesian Statistics
Bayesian statistics is a branch of statistics that uses probability to model uncertainty and update beliefs based on new evidence, applying Bayes' theorem to combine prior knowledge with observed data. It provides a framework for making inferences, predictions, and decisions in a probabilistic manner, often used in fields like machine learning, data science, and experimental research. Unlike frequentist statistics, it treats parameters as random variables with probability distributions.
Developers should learn Bayesian statistics when working on projects involving probabilistic modeling, uncertainty quantification, or adaptive systems, such as in machine learning (e.g., Bayesian networks, Gaussian processes), A/B testing, or risk analysis. It is particularly useful for scenarios where prior information is available or when making iterative updates based on streaming data, as it offers a coherent approach to handling incomplete or noisy information.