Prior Distribution
A prior distribution is a probability distribution that expresses beliefs or assumptions about an unknown parameter before observing any data, used primarily in Bayesian statistics. It represents the initial state of knowledge or uncertainty regarding a parameter, which is then updated with observed data to form a posterior distribution through Bayes' theorem. Priors can be informative (based on existing knowledge) or non-informative (minimally influential, like uniform distributions).
Developers should learn about prior distributions when working with Bayesian machine learning, probabilistic programming, or statistical inference, as they are fundamental to modeling uncertainty and incorporating domain knowledge. They are essential in applications like A/B testing, recommendation systems, and risk analysis, where prior beliefs can improve model accuracy and decision-making. Understanding priors helps in choosing appropriate models in frameworks like PyMC, Stan, or TensorFlow Probability.