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