Bayesian Probability
Bayesian probability is a statistical interpretation of probability that quantifies uncertainty as a degree of belief, updated in light of new evidence using Bayes' theorem. It contrasts with frequentist probability by incorporating prior knowledge and treating parameters as random variables rather than fixed unknowns. This approach is foundational in fields like machine learning, data science, and decision theory for making probabilistic inferences.
Developers should learn Bayesian probability when working on projects involving uncertainty, such as predictive modeling, A/B testing, or recommendation systems, as it allows for flexible updating of beliefs with data. It is particularly useful in machine learning for Bayesian networks, spam filtering, and natural language processing, where prior information can improve accuracy. In data science, it supports robust decision-making under uncertainty, making it essential for roles in AI, analytics, and research.