| 11. | Note that this is different from the likelihood function of the truncated regression model.
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| 12. | For simple models, an analytical formula for the likelihood function can typically be derived.
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| 13. | For such a model, the likelihood function depends on at least one independent random variables.
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| 14. | In such a situation, the likelihood function factors into a product of individual likelihood functions.
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| 15. | In such a situation, the likelihood function factors into a product of individual likelihood functions.
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| 16. | In addition, several common distributions have likelihood functions that contain products of factors involving exponentiation.
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| 17. | Therefore, Fisher information is a measure of the curvature of the log likelihood function of ?.
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| 18. | For example, some likelihood functions are for the parameters that explain a collection of statistically independent observations.
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| 19. | If you pick 4 or more tickets, the likelihood function has a well defined standard deviation too.
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| 20. | Therefore, using the same indices to denote distributions, we can write the log-likelihood function thus:
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