Hi,
I would like to introduce a new distribution class for an AR(1) process following the existing implementation of the GaussianRandomWalk distribution. However, I do not understand the role of the ‘reparametrized_params’, e.g.
reparametrized_params = [‘scale’]
Could someone clarify this part a bit?
I noticed that other distributions contain similar lists but I do not understand which variables should be contained here and which not.
For example Gamma distribution has two positive definite variables but only ‘rate’ is listed as re-parameterized.
class Gamma(Distribution):
arg_constraints = {‘concentration’: constraints.positive,
‘rate’: constraints.positive}
support = constraints.positive
reparametrized_params = [‘rate’]
…