I want to define my own abstract model. Say I have a probability distribution with

`p(x) = 7 *exp(-x/4) + 4* exp(-x/6) - 7*exp(-(x-4)^2/64)`

and I do not know if they are independent or not, therefore, I cannot compute them together analytically and write it as a single normal distribution. How do I define this as my abstract model in Julia.

You should be more specific about libraries / goals you try to use. It is difficult to help you like that. Julia does not have any model per se, so you likely intend to use some library and define the model specific for some library. I guess since you are talking about probabilities, it is about Turing.jl?

You’re right. Gladly I figured it out. But we really need more examples of the uses cases of the library.

Usually people want to do something and are looking for a library, are you looking for any library to do random things?

I am trying to figure out how to use Turing.sample with an abstract model. The problem is I cannot define my abstract model into a distribution format which I can sample from using Turing.sample. The distribution is given at the top. I am trying to use Distributions.MixtureModel to represent the distribution that I have. Can you show me an example or something similar?