Hi,
I'm using PyMC to sample from the distribution of two parameters, one whose
domain is about [0,2] and the other [300,600]. Since they are correlated,
I'm block updating the two together, ie. self.parameters('AB', init_val =3D
[1, 500])
It appears that doing this is not efficient, in the sense that the jumps fo=
r
B are of the same order than for A. Hence, it takes a very long time to
converge to the stationary distribution. Taking the log of B has allowed me
to cut the number of simulations dramatically. Is this normal ?
I thought that each parameter would get its own jump variance, with a
correlation coefficient linking the two.
Thanks,
David
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