Sampling from custom function

Questions
1

i have obtained samples from posterior distribution using normal prior,
now i want to form a custom function that takes - target prior as input multiplied with posterior samples divided by normal prior that was used for generation of posterior samples.

Using the above new custom function i want to again sample new posterior distribution. How to do this?

  1. How to know the prior used by pymc3 algorithm for computing posterior samples.

The prior for the model parameters will be a normal distribution

family = pm.glm.families.Normal()

# Creating the model requires a formula and data (and optionally a family)

pm.GLM.from_formula(formula, data = X_train, family = family)

# kwargs = dict(target_accept=.8)

# Perform Markov Chain Monte Carlo sampling

normal_trace = pm.sample(draws=1000, tune = 1000, target_accept = 0.8, init = "auto")

i have used the above code to generate posterior samples.