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위키데이터

• ID : Q327069

말뭉치

1. Why do we care about conditional probability?
2. Conditional probability is used in many areas, in fields as diverse as calculus, insurance, and politics.
3. Conditional probability is defined as the likelihood of an event or outcome occurring, based on the occurrence of a previous event or outcome.
4. Conditional probability can be contrasted with unconditional probability.
5. Conditional probability: p(A|B) is the probability of event A occurring, given that event B occurs.
6. Bayes' theorem, named after 18th-century British mathematician Thomas Bayes, is a mathematical formula for determining conditional probability.
7. It is important to note that conditional probability itself is a probability measure, so it satisfies probability axioms.
8. In fact, all rules that we have learned so far can be extended to conditional probability.
9. This format is particularly useful in situations when we know the conditional probability, but we are interested in the probability of the intersection.
10. (the conditional probability of A given B) typically differs from P(B|A).
11. Conditional probability can be defined as the probability of a conditional event A B {\displaystyle A_{B}} .
12. Here, in the earlier notation for the definition of conditional probability, the conditioning event B is that D 1 + D 2 ≤ 5, and the event A is D 1 = 2.
13. This conditional probability measure also could have resulted by assuming that the relative magnitude of the probability of A with respect to X will be preserved with respect to B (cf.
14. The laws of conditional probability ensure that Bayesian updating has features that seem desirable in any dogmatic learning rule.
15. Conditional probability is the probability of an event occurring given that another event has already occurred.
16. Another way of calculating conditional probability is by using the Bayes’ theorem.
17. A conditional probability is the probability of an event, given some other event has already occurred.
18. Although typically we expect the conditional probability \(P(A\mid B)\) to be different from the probability \(P(A)\) of \(A\), it does not have to be different from \(P(A)\).
19. In this paper we show part of the results of a larger study1 that investigates conditional probability problem solving.
20. In particular, we report on a structure-based method to identify, classify and analyse ternary problems of conditional probability in mathematics textbooks in schools.
21. Why and how should we prepare our students in conditional probability at secondary school?
22. From this perspective, it is necessary to explore contexts and phenomena in which conditional probability is actually involved.
23. The following diagram shows the formula for conditional probability.
24. It follows that the formula for conditional probability 'holds'.
25. A Bayes' problem can be set up so it appears to be just another conditional probability.
26. We are now up to speed with marginal, joint and conditional probability.
27. This alternate calculation of the conditional probability is referred to as Bayes Rule or Bayes Theorem, named for Reverend Thomas Bayes, who is credited with first describing it.
28. After hearing that Jean is to be executed, Sam reasons that, since either he or Chris must be the other one, the conditional probability that he will be executed is 1/2.
29. Note that when we evaluate the conditional probability, we always divide by the probability of the given event.
30. To answer this, we may compare the overall probability of having pierced ears to the conditional probability of having pierced ears, given that a student is male.
31. Conditional probability is the probability of one thing being true given that another thing is true, and is the key concept in Bayes' theorem.
32. With a state and for an , another state ν is called a conditional probability of under if holds for all with .
33. The unique conditional probability of μ under is denoted by and, in analogy with classical mathematical probability theory, is often written instead of with .
34. For the proof of (2.2), suppose that the state ν on is a version of the conditional probability of the state μ under and use the identity .
35. Therefore the conditional probability must have this shape and its uniqueness is proved.

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위키데이터

• ID : Q327069

Spacy 패턴 목록

• [{'LOWER': 'conditional'}, {'LEMMA': 'probability'}]