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text: '[summary: *Bayesian updating* or *Bayesian belief revision* is a way of changing [1rf probabilistic] beliefs, in response to evidence, in a way that obeys [-1bv] and in particular [1lz].\n\nWhen we observe a piece of evidence that is more likely to be seen if X is true than if X is false, we should assign more credence to X. For example, if I take a random object from your kitchen, and then I tell you this object is sharp, you should assign more credence than you did previously that it's a knife (even though forks are also sharp, so you won't be certain).\n\nFor more on this subject see [1zq the Arbital guide to Bayes's rule].]\n\nA Bayesian *update* or *belief revision* is a change in [1rf probabilistic] beliefs after gaining new knowledge. For example, after observing a patient's test result, we might revise our probability that a patient has a certain disease. If this belief revision obeys [1lz Bayes's Rule], then it is called [1r8 Bayesian].\n\nBayesian belief updates have a number of other interesting properties, and exemplify many key principles of clear reasoning or [9l rationality]. Mapping Bayes's Rule onto real-life problems of encountering new evidence allows us to reproduce many intuitive features that have been suggested for "how to revise beliefs in the face of new evidence".\n\n- [21v (Extra)ordinary claims require (extra)ordinary evidence.]\n- The scientific virtues of [220 falsifiability, advance prediction, boldness, precision, and falsificationism] can be seen in a Bayesian light.\n\n',
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Summary: '*Bayesian updating* or *Bayesian belief revision* is a way of changing [1rf probabilistic] beliefs, in response to evidence, in a way that obeys [-1bv] and in particular [1lz].\n\nWhen we observe a piece of evidence that is more likely to be seen if X is true than if X is false, we should assign more credence to X. For example, if I take a random object from your kitchen, and then I tell you this object is sharp, you should assign more credence than you did previously that it's a knife (even though forks are also sharp, so you won't be certain).\n\nFor more on this subject see [1zq the Arbital guide to Bayes's rule].'
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