例句與造句

1. For each choice of base, the logit function takes values between negative and positive infinity.
2. Closely related to the logit function ( and logit model ) are the probit function and probit model.
3. The intuition for transforming using the logit function ( the natural log of the odds ) was explained above.
4. The logistic function is the inverse of the natural logit function and so can be used to convert the logarithm of odds into a probability.
5. When the function's parameter represents a probability, the logit function gives the "'log-odds "', or the logarithm of the odds.
6. It's difficult to find logit function in a sentence. 用logit function造句挺難的
7. This can be shown as follows, using the fact that the cumulative distribution function ( CDF ) of the standard logistic distribution is the logistic function, which is the inverse of the logit function, i . e.
8. If e _ n \ sim \ operatorname { Logistic } ( 0, 1 ), i . e . distributed as a standard logistic distribution with mean 0 and scale parameter 1, then the corresponding quantile function is the logit function, and
9. Instead of a copy you could also use a link to Wikipedia : Reference desk / Archives / Mathematics / 2007 March 19 # derivative of the inverse of a logit function, which right now is still a redlink but in a few days will become a link to this section .-- Talk 20 : 31, 19 March 2007 ( UTC)
10. The reason for the use of the probit model is that a constant scaling of the input variable to a normal CDF ( which can be absorbed through equivalent scaling of all of the parameters ) yields a function that is practically identical to the logit function, but probit models are more tractable in some situations than logit models . ( In a Bayesian setting in which normally distributed prior distributions are placed on the parameters, the relationship between the normal priors and the normal CDF link function means that a probit model can be computed using Gibbs sampling, while a logit model generally cannot .)