[Solved]Create custom joint probability distribution

[fehiepsi]

@neerajprad has a great solution for it, you can check it here:

Am I using HMC/NUTS/MCMC correctly?

As an alternate approach, I think you could also define a distribution class with log_prob that gives you unnormalized density and use it in your model. Sample code: import torch from torch.distributions import constraints import pyro from pyro.distributions import TorchDistribution from pyro.infer.mcmc import MCMC, NUTS class UnnormalizedDist(TorchDistribution): support = constraints.real has_rsample = False def __init__(self): self.mu = torch.tensor(2.) self.st…