Bayesian Methods
Bayesian methods are a statistical approach based on Bayes' theorem, which updates the probability of a hypothesis as more evidence or data becomes available. They involve using prior knowledge (priors) combined with observed data to form posterior distributions, enabling probabilistic inference and decision-making under uncertainty. These methods are widely applied in fields like machine learning, data science, and scientific research for tasks such as parameter estimation, prediction, and model comparison.
Developers should learn Bayesian methods when working on projects that require handling uncertainty, making predictions with limited data, or incorporating prior domain knowledge into models, such as in Bayesian machine learning, A/B testing, or risk analysis. They are particularly useful in data science for building robust statistical models, in AI for probabilistic programming (e.g., with PyMC or Stan), and in research settings where interpretability and uncertainty quantification are critical, like in healthcare or finance applications.