A probability measure that cannot shift its assignments in response to accumulated negative instances fails the basic Bayesian coherence condition that posteriors must differ from priors given relevant evidence.

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Bayesian: # Bayesian Bayesian refers to a way of thinking about probability and uncertainty that starts with what you already believe, then updates that belief as you get new evidence. Rather than treating probability as just counting how often something happens, it treats it as a measure of how confident you are about something. This approach is named after Thomas Bayes, an 18th-century mathematician, and it's widely used today in everything from medical testing to spam filters because it mirrors how humans naturally learn and change their minds.
Bayesian coherence(in epistemology (the study of knowledge)): A rule stating that your beliefs should be logically consistent with each other and should change in a rational, mathematical way when you get new information.
Posteriors(how confident you should be in a belief after seeing the evidence): Your updated beliefs or confidence level after considering new evidence.
Priors(as used in probability and epistemology): Your initial assumptions or beliefs about how likely something is before you consider new evidence (like guessing the probability before looking at the facts).

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