Decision-making under uncertainty refers to the process of making choices without having complete information about the outcomes, probabilities, or conditions involved. This type of decision-making often requires evaluating risks, estimating potential outcomes, and incorporating varying degrees of belief or confidence into the decision process. In Bayesian statistics, this is closely connected to the use of credible intervals, which provide a range of plausible values for parameters based on observed data.
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