Greetings, Is it possible to use evidence in a Winbug model? For example, a random variable in a model has been observed, and I'd like to update the other variables in the model, pretty much the same update perfomed in tools like Smile, or other inference software.
Gibbs sampling is supposed to use observed values in full conditional distribution when there observations/evidence are entered into the model, but I am not sure if Winbugs allows this.
edit to clarify:
The winbug documentation says a stochastic node is a data node if it has been observed, but a stochastic node is described via a distribution as in some_var ~ dbin(theta,n) if it has been observed, then I'd like to tell this to winbugs without losing the semantics of the stochastic node, expressing something like "some_var has this distribution, and it has been observed to have this particular value". so how do I do that? by declaring some_var as I've done above and than setting a value to it as in some_var = 5 ? Would that express what I want to express?
In this case, in each observation of a node in a bayesian network, I'd need to redefine the winbugs model, (quite likely) replacing initial values of the unobserved nodes with the outcomes of the previous simulation.
In short, I'm trying to understand how to perform updates on a Bayesian network similar to message passing in exact inference, but using Gibbs sampling instead, via Winbugs.