Hopefully a quicker question than my last…
I have a dataset of observations spanning several years, and predictions to make on today’s observations. I’d like to discount the influence of the older observations, assuming that they are less relevant.
I can’t remember where (hence my question here) I saw a method to apply a bias by directly multiplying the logp. I’ve tried it and it seems to work, but does anyone have strong opinions on why this might not be a good idea?
the critical part of the model spec:
pi_dist = pm.Bernoulli.dist(p=psi)
pi_like = pm.Potential('pi_like', pi_dist.logp(y_pi) * x_psi_recency_bias)
where (just for color):

psi
is a float in range [0,1] 
y_pi
is an int in {0, 1} 
x_psi_recency_bias
is a float in range (0, 1] where 1 is today’s observations, and more historical years are closer and closer to 0  all 3 are vectors of course
Thanks!