reno.pymc.to_pymc_model

reno.pymc.to_pymc_model(model, observations=None, steps=None)

Generate a pymc model for bayesian analysis of this system dynamics model. The general idea is that this creates corresponding pymc variables (or distributions as relevant) for each stock/flow/var in the model, and sets up the full simulation sequence computations based on the generated step function from pt_sim_step().

Sampling with priors should be equivalent to running the system dynamics model normally (this is essentially “forward simulation mode”.) Add observations to the pymc model variables and sample from posterior to run bayesian analysis/determine how distributions of any other variables may be affected.

Parameters:

• model (Model)

• observations (list[Observation])

• steps (int)

Return type:

Model