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khafra comments on Open thread, Sept. 29 - Oct.5, 2014 - Less Wrong
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Question for AI people in the crowd: To implement Bayes' Theorem, the prior of something must be known, and the conditional likelihood must be known. I can see how to estimate the prior of something, but for real-life cases, how could accurate estimates of P(A|X) be obtained?
Also, we talk about world-models a lot here, but what exactly IS a world-model?
Not quite the way I'd put it. If you know the exact prior for the unique event you're predicting, you already know the posterior. All you need is a non-pathologically-terrible prior, although better ones will get you to a good prediction with fewer observations.