Decision-making under uncertainty refers to the process of making choices when the outcomes of those choices are not known and cannot be predicted with certainty. This involves evaluating potential scenarios, considering probabilities, and weighing the risks and benefits associated with each decision. Understanding how to use prior and posterior distributions is key to refining these decisions, as they help in updating beliefs and making informed choices based on available data.
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