Which Bayesian procedure describes updating confidence as new information arrives?

• A. Start with prior probability; apply likelihood of new data; compute posterior probability; adjust confidence; document update and assumptions.
• B. Start with posterior probability; ignore prior; update with random data.
• C. Use a static probability; never update.
• D. Use a qualitative assessment only.

Answer: (A)

Bayesian updating is about revising what you believe when new evidence comes in. You start with a prior probability that expresses your initial belief. Then you assess how likely the new information is given different possible states, which is the likelihood. By combining the prior with this new data through Bayes’ rule, you obtain a posterior probability—the updated level of confidence after taking the new information into account. It’s also important to document how you updated and what assumptions you made so the process is transparent and repeatable. This sequence—begin with a belief, weigh the new data, update your belief, and note the method—is exactly how confidence should be adjusted as information arrives. The other options miss key elements: they either ignore the prior, ignore how new data should influence beliefs, rely on a static probability that never updates, or depend only on qualitative judgment without probabilistic updating.