I am running a model with the following priors:

A =pm.TruncatedNormal(‘A’, mu=850, sd=400)

B = pm.TruncatedNormal(‘B’, mu=-0.096, sd=0.05, upper=-0.01)

C =pm.TruncatedNormal(‘C’, mu=950, sd=500, lower=1)

D = pm.TruncatedNormal(‘D’, mu=-0.093, sd=0.046, upper=-0.01)

E = pm.TruncatedNormal(‘E’, mu=3.94, sd=2, lower=0.1)

F = pm.TruncatedNormal(‘F’, mu=6.25, sd=3, lower=0.5)

G = pm.Uniform(‘G’, lower=0.1, upper=1)

The model runs well for my first set of observed data. When I try to use a second set of data that is not a close to the likeihood function, I the model crashes with the code SamplingError(“Bad initial energy”). I’ve tried re-paramaterizing, and init=‘adapt_diag’, but none of that has helped. I’ve seen advice to standardize the prior distributions. But could it just be the case that the likelihood function is not a good predictor of the second set of data?

Thank You