Probabilistic Programming
Probabilistic programming is a programming paradigm that enables developers to build and reason about probabilistic models using code. It combines probability theory with programming languages to represent uncertainty and perform Bayesian inference automatically. This approach allows for the creation of complex statistical models that can learn from data and make predictions with quantified uncertainty.
Developers should learn probabilistic programming when working on projects involving uncertainty, such as machine learning, data science, risk analysis, or decision-making systems. It is particularly useful for building Bayesian models, performing statistical inference, and handling incomplete or noisy data, as it automates complex mathematical computations and provides a flexible framework for modeling real-world phenomena.