Gibbs sampling in Analytica
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- it is easy to operationalise Bayesian updating within a model.
The input should contain data and likelihood and prior distributions. This should be able to build for several model nodes, as they may serve as data for each other.
The output should contain a posterior distribution.
A tricky question is, what to do with probabilistic nodes, as in Analytica the default is that nodes are functional nodes. Can the definition of a node be described probabilistically?
Gibbs sampler should be some kind of function. It should have a variable list and a likelihood list as input parameters.