Bayesian decision theory

Bayesian decision theory is a framework for making decisions under uncertainty. It combines prior knowledge (what we believe before seeing new evidence) with new information (data or observations) to update our beliefs. This process, known as Bayesian updating, helps us calculate the probability of various outcomes. By weighing the potential benefits and risks of different choices, we can make informed decisions that reflect both our initial beliefs and the latest evidence. Essentially, it’s a way to systematically incorporate new data into our decision-making process, leading to better choices based on the most current understanding.