def observed(cls, parent: Gaussian, child: torch.tensor,
variance: torch.Tensor):
child = Gaussian.observed(child)
obj = cls(parent, child, variance)
obj._observed = True
return obj
# prior case shape = (2, 3) child = Gaussian.from_shape(shape, 1., 2.) self = GaussianFactor.prior(child, 0., 1.) self.forward() self.backward() self self.to_elbo() # observed case shape = (2, 3) parent = Gaussian.from_shape(shape, 0., 1.) child = torch.randn(shape) child[0, 0] = np.nan Gaussian.observed(child) self = GaussianFactor.observed(parent, child, 1.) self.forward() self.backward() self self.to_elbo() # ----------------------------------------------------------------------------- # Logistic import torch import numpy as np from NNVI.vmp.utils import sigmoid_integrals from NNVI.vmp.bernoulli import Bernoulli from NNVI.vmp.gaussian import Gaussian from NNVI.vmp.factors import Logistic