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Bernoulli and Bayesian probabilities
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[QUOTE="andrewkirk, post: 6446495, member: 265790"] Yes. "Accuracy" measures the difference between the estimate and the true population parameter. Your data sample gives a MLE estimate of 0.75. You have done two different Bayesian calcs, call them B1 and B2, producing estimates of 0.7333 and 0.6. If you line these up on a number line, you can see that : [LIST] [*]MLE is most accurate if the population parameter is greater than (0.7333 + 0.75) / 2, approx 0.742 [*]B1 is most accurate if the population parameter is between (0.6 + 0.7333) / 2 and (0.7333 + 0.75) / 2 (approx between 0.67 and 0.742) [*]B2 is most accurate if the population parameter is less than (0.6 + 0.7333) / 2 (approx 0.67). [/LIST] Any of those three could be true given the information provided in the OP! As [USER=494486]@ChrisVer[/USER] points out, if the question doesn't state the population parameter's value, it is impossible to answer. [/QUOTE]
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Bernoulli and Bayesian probabilities
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