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Imprecise probability is a field that studies how to represent and reason about uncertainty when precise probabilities are not justified by the evidence. Books in this category cover theories and tools that use ranges, sets of distributions, or non-additive measures to capture limited data, ambiguity, and model or expert disagreement. Topics typically include credal sets and lower/upper probabilities, robust Bayesian analysis, Dempster–Shafer belief functions, possibility theory, and decision criteria for acting under partial information. The category spans foundational texts, methodological works on inference, learning, elicitation, and sensitivity analysis, and applications in areas such as risk and reliability, engineering, AI and machine learning, environmental and health modeling, and public policy.

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