Bayesian Inference

Bayesian inference is a statistical method that applies Bayes' theorem to update the probability of a hypothesis as more evidence or information becomes available. It is characterized by its use of prior distributions, likelihood functions, and posterior distributions, allowing for a coherent way to incorporate new data into existing beliefs. Common use cases include decision-making in uncertain environments, machine learning algorithms, and predictive modeling where prior knowledge is essential. This approach is fundamental in various fields, including data science, machine learning, and artificial intelligence, as it provides a robust framework for reasoning under uncertainty.