Bayesian Beta Posterior
A conjugate prior method for modeling the probability of binary outcomes, expressing belief about the success probability through parameters alpha (successes plus prior) and beta (failures plus prior). It updates automatically as evidence accumulates and is computationally very simple.
Timeline (last 90 days)
The decision layer backend adopts a Bayesian Beta posterior, updating based on historical results in memory, and confidence moves as evidence accumulates.
In the Bayesian Beta posterior, the parameter α represents the observed number of successes plus the prior and β represents the number of failures plus the prior; the prior can be set to an uninformative Beta(1,1), and the posterior automatically accumulates α and β as evidence updates.
All Facts (2)
The decision layer backend adopts a Bayesian Beta posterior, updating based on historical results in memory, and confidence moves as evidence accumulates.
50%UnverifiedIn the Bayesian Beta posterior, the parameter α represents the observed number of successes plus the prior and β represents the number of failures plus the prior; the prior can be set to an uninformative Beta(1,1), and the posterior automatically accumulates α and β as evidence updates.
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