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 请问小衲，什么是conditional probability？路巴等的调查报告中在哪个地方涉及了条件概率？ |
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最後一次解釋 -- 唐好色 - (355 Byte) 2010-7-26 周一, 下午11:52 (85 reads) |
若迷
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作者：若迷 在 罕见奇谈 发贴, 来自 http://www.hjclub.org
【小衲: 大科學家中(conditional)有7%(probability)信神是個很高的數字. (括弧中的英文是我的加注).
若迷: 太多, 太少, 太高, 太低, 隨機抽選, 抽選隨機, 小衲笨蛋...
老唐: 小衲這麼說的原因在於這是個 conditional probability.
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就這樣. 小衲好像是吃這行飯的. 他的建議不錯----閣下還是先找個懂行的朋友, 向他請教. 查維基也要先有點基礎知識才行. 對了, 你最好別帶著爭勝的心態去請教, 不然的話, 你的朋友可能會笑笑走開.】
这回让你撞上了，咱就是懂行的。以前还只是对你喜欢瞎说印象蛮深，这回我算是把你瞧得铁定了，你是啥都不懂，又啥都敢胡说。不想打字了，给你看点维基上的摘录吧，看看你不懂装懂到什么程度。可惜软件不同，公式都无法拷贝上来，要更仔细看，就进这个链接：
http://en.wikipedia.org/wiki/Conditional_probability
Conditional probability is the probability of some event A, given the occurrence of some other event B. Conditional probability is written P(A|B), and is read "the (conditional) probability of A, given B" or "the probability of A under the condition B". When in a random experiment the event B is known to have occurred, the possible outcomes of the experiment are reduced to B, and hence the probability of the occurrence of A is changed from the unconditional probability into the conditional probability given B.
Joint probability is the probability of two events in conjunction. That is, it is the probability of both events together. The joint probability of A and B is written or
Marginal probability is then the unconditional probability P(A) of the event A; that is, the probability of A, regardless of whether event B did or did not occur. If B can be thought of as the event of a random variable X having a given outcome, the marginal probability of A can be obtained by summing (or integrating, more generally) the joint probabilities over all outcomes for X. For example, if there are two possible outcomes for X with corresponding events B and B', this means that . This is called marginalization.
In these definitions, note that there need not be a causal or temporal relation between A and B. A may precede B or vice versa or they may happen at the same time. A may cause B or vice versa or they may have no causal relation at all. Notice, however, that causal and temporal relations are informal notions, not belonging to the probabilistic framework. They may apply in some examples, depending on the interpretation given to events.
Conditioning of probabilities, i.e. updating them to take account of (possibly new) information, may be achieved through Bayes' theorem. In such conditioning, the probability of A given only initial information I, P(A|I), is known as the prior probability. The updated conditional probability of A, given I and the outcome of the event B, is known as the posterior probability, P(A|B,I).
作者：若迷 在 罕见奇谈 发贴, 来自 http://www.hjclub.org |
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- 最後一道習題 -- 唐好色 - (100 Byte) 2010-7-27 周二, 上午12:24 (128 reads)
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