pymc.model.transform.conditioning.observe

pymc.model.transform.conditioning.observe(model, vars_to_observations)

Convert free RVs or Deterministics to observed RVs.

Parameters:

model: PyMC Model

vars_to_observations: Dict of variable or name to TensorLike

Dictionary that maps model variables (or names) to observed values. Observed values must have a shape and data type that is compatible with the original model variable.

Returns:

new_model: PyMC model

A distinct PyMC model with the relevant variables observed. All remaining variables are cloned and can be retrieved via new_model[“var_name”].

Examples

import pymc as pm

with pm.Model() as m:
    x = pm.Normal("x")
    y = pm.Normal("y", x)
    z = pm.Normal("z", y)

m_new = pm.observe(m, {y: 0.5})

Deterministic variables can also be observed. This relies on PyMC ability to infer the logp of the underlying expression

import pymc as pm

with pm.Model() as m:
    x = pm.Normal("x")
    y = pm.Normal.dist(x, shape=(5,))
    y_censored = pm.Deterministic("y_censored", pm.math.clip(y, -1, 1))

new_m = pm.observe(m, {y_censored: [0.9, 0.5, 0.3, 1, 1]})