pymc.model.transform.conditioning.remove_value_transforms#
- pymc.model.transform.conditioning.remove_value_transforms(model, vars=None)[source]#
Remove the value variables transforms in the model.
- Parameters:
- model
Model
- vars
Model
variables
, optional Model variables for which to remove transforms. Defaults to all transformed variables
- model
- Returns:
- new_model
Model
Model with the removed transformed value variables
- new_model
Examples
Extract untransformed space Hessian after finding transformed space MAP
import pymc as pm from pymc.model.transform.conditioning import remove_value_transforms with pm.Model() as transformed_m: p = pm.Uniform("p", 0, 1) w = pm.Binomial("w", n=9, p=p, observed=6) mean_q = pm.find_MAP() with remove_value_transforms(transformed_m) as untransformed_m: new_p = untransformed_m["p"] std_q = ((1 / pm.find_hessian(mean_q, vars=[new_p])) ** 0.5)[0] print(f" Mean, Standard deviation\\np {mean_q['p']:.2}, {std_q[0]:.2}") # Mean, Standard deviation # p 0.67, 0.16