In Bayes' Theorem, the term P(A) (before any new evidence) is referred to as the Blank 1 probability.

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In Bayes' Theorem, we assess how our beliefs update with new evidence. The probability P(A) evaluated before observing any data is called the prior probability, reflecting our initial belief about the event A before seeing the evidence. Since the question asks for the term used for P(A) before new evidence, this is the standard nomenclature in Bayesian inference: the prior probability.

Key concept: prior probability represents the state of knowledge prior to observing any data, serving as the starting point for updating beliefs via Bayes' rule.

If you were to think about alternatives (even though none are shown here), you might consider how a likelihood term functions in Bayes' Theorem, which is P(B|A) and describes the probability of evidence given A, or how the posterior combines prior and likelihood after observing data. The prior itself is distinct because it does not incorporate the new evidence yet; it encodes pre-observation beliefs.

In summary, the blank to be filled with the standard term for P(A) before new evidence is the prior probability, representing the initial belief about event A prior to incorporating data.

STP420 - Probability and Statistics STP420 - Module 4 - Quiz STP420 - Probability and Statistics STP420 - Module 4 - Quiz

In Bayes' Theorem, the term P(A) (before any new evidence) is referred to as the Blank 1 probability.

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