Thank you for reply and I read it.

The above page describes the two methods to remove divergence, that is decreasing the step size and change the model description to exactly equivalent oneâ€¦ And I attempted the first method but it did not work well.

I set `control= list(adapt_delota= 0.9999 and this treatment does not have efficacy for my model. So, I think my model description should be changed but I am not sure haw to change.

To tell the truth, I tried the second method, i.e. changing the description of my model,

Some parameter in my model has very strong bias,

The above page shows the only one example to reduce the divergence, that is Gaussian case with linear parametrization, I want to know, binomial case. My case is Binomial distributionâ€™s. In this case, parameters is defined by some function , then what should we do for centering parameters.

X \sim \text{Binomial}(prob = foo(\theta) ,N)

where `foo`

is a some non linear function of \theta which is a model parameter and N is a iteration number of Bernoulli trials. I wonder it is possible to centering `foo()`

in the case of a non linear function Or, we need to approximate `foo()`

by some linear function to centering? Such a approximation will generate a new bias.

Centering is difficult for non linear functionsâ€¦