A more universal entropy calculation method for sampling based inference #151
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In the previous implementation of sampling based inference, the free energy calculation was not possible in smoothing tasks unless the hidden states are Gaussian distributed. With this PR, we generalise the entropy calculation to broader range of inference tasks such as smoothing in switching state space models where the forward messages, commute through equality nodes on the most upper chain, are constituted by SampleList. The entropy calculation is carried out by conveying the proposal distribution and integrand information on SampleList messages. When a SampleList message collides with a probability distribution or its pdf, our method updates the integrand. This allows entropy calculation not only for those cases where two probability distribution or pfd messages collide, i.e. m1(x).m2(x), but also for the cases when multiple collisions are required to approximate posteriors, e.g. q(x) \propto m1(x).m2(x).m3(x).m4(x).