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In probability theory, a normalizing constant or normalizing factor is used to reduce any nonnegative function whose integral is finite to a probability density function.
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| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Normalizing constant | is a | constant by which an everywhere non-negative function must be multiplied so the area under its graph is 1 | 0.90 | text |
| Normalizing constant | related to Bayes' theorem | Bayes | 0.60 | section |
| Normalizing constant | related to Bayes' theorem | Proportional | 0.60 | section |
| Normalizing constant | related to Bayes' theorem | In | 0.60 | section |
| Normalizing constant | related to Bayes' theorem | H0 | 0.60 | section |
| Normalizing constant | related to Bayes' theorem | Since | 0.60 | section |
| Normalizing constant | related to Bayes' theorem | It | 0.60 | section |
| Normalizing constant | related to Bayes' theorem | For | 0.60 | section |
| Normalizing constant | related to Bayes' theorem | Methods | 0.60 | section |
| Normalizing constant | related to Bayes' theorem | Monte Carlo | 0.60 | section |
| Normalizing constant | related to Definition | In | 0.60 | section |
| Normalizing constant | related to Examples | If | 0.60 | section |
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