bayesian setting造句

A parameter in Bayesian setting is a random variable.
From a philosophical perspective, the loss function in a regularization setting plays a different role than the likelihood function in the Bayesian setting.
To address this issue, we can consider conditional power in a Bayesian setting by considering the treatment effect parameter to be a random variable.
In addition, in a Bayesian setting, the mixture weights and parameters will themselves be random variables, and prior distributions will be placed over the variables.
In a Bayesian setting, it has been argued that the problem of multiple treatments can be incorporated in multilevel models, which addresses the multiple comparisons problems while yielding more efficient estimates if the model specification is correct.
It's difficult to find bayesian setting in a sentence. 用bayesian setting造句挺难的

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 .)