Joint distribution p(x, y)'s marginal probability p(x)

pxy[i, j] indicates p(x=x_i, y=y_i).
p(x) is a torch with two values: p(x=x_0) and p(x=x1).Save to the Tensor, px.

pxy = torch.softmax(torch.randn(6), dim-0).reshape(2, 3) print (pxy) # TODO: ?? ?? ???? px - None = print (px) tensor ([[0.4828, 0.0203, 0.3203], [0.0497, 0.0882, 0.0387]]) None

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Joint distribution p(x, y)'s marginal probability p(x)

pxy[i, j] indicates p(x=x_i, y=y_i).
p(x) is a torch with two values: p(x=x_0) and p(x=x1).Save to the Tensor, px. pxy = torch.softmax(torch.randn(6), dim-0).reshape(2, 3)
print (pxy)
# TODO: ?? ?? ????
px - None
=
print (px)
tensor ([[0.48

pxy = torch.softmax(torch.randn(6), dim-0).reshape(2, 3) print (pxy) # TODO: ?? ?? ???? px - None = print (px) tensor ([[0.4828, 0.0203, 0.3203], [0.0497, 0.0882, 0.0387]]) None

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