Bayesian Approximation

Bayesian approximation is a way to estimate complex probabilistic models by updating our initial beliefs with new data. It relies on Bayes' theorem, which combines prior knowledge and evidence to produce a refined understanding, called a posterior distribution. Because exact calculations can be difficult, approximation methods like variational inference or Monte Carlo sampling are used to find practical solutions. This approach helps in making predictions, managing uncertainty, and improving models when full calculations are computationally intensive, enabling more accurate decision-making in uncertain situations.