pymc.model.core.Deterministic#

pymc.model.core.Deterministic(name, var, model=None, dims=None)[source]#

Create a named deterministic variable.

Deterministic nodes are only deterministic given all of their inputs, i.e. they don’t add randomness to the model. They are generally used to record an intermediary result.

Parameters:

namestr: Name of the deterministic variable to be registered in the model.
vartensor_like: Expression for the calculation of the variable.
modelModel, optional: The model object to which the Deterministic variable is added. If None is provided, the current model in the context stack is used.
dimsstr or tuple of str, optional: Dimension names for the variable.

Returns:

vartensor_like: The registered, named variable wrapped in Deterministic.

Notes

Even though adding a Deterministic node forces PyMC to compute this expression, which could have been optimized away otherwise, this doesn’t come with a performance cost. Indeed, Deterministic nodes are computed outside the main computation graph, which can be optimized as though there was no Deterministic nodes. Whereas the optimized graph can be evaluated thousands of times during a NUTS step, the Deterministic quantities are just computeed once at the end of the step, with the final values of the other random variables.

Examples

Indeed, PyMC allows for arbitrary combinations of random variables, for example in the case of a logistic regression

with pm.Model():
    alpha = pm.Normal("alpha", 0, 1)
    intercept = pm.Normal("intercept", 0, 1)
    p = pm.math.invlogit(alpha * x + intercept)
    outcome = pm.Bernoulli("outcome", p, observed=outcomes)

but doesn’t memorize the fact that the expression pm.math.invlogit(alpha * x + intercept) has been affected to the variable p. If the quantity p is important and one would like to track its value in the sampling trace, then one can use a deterministic node:

with pm.Model():
    alpha = pm.Normal("alpha", 0, 1)
    intercept = pm.Normal("intercept", 0, 1)
    p = pm.Deterministic("p", pm.math.invlogit(alpha * x + intercept))
    outcome = pm.Bernoulli("outcome", p, observed=outcomes)

These two models are strictly equivalent from a mathematical point of view. However, in the first case, the inference data will only contain values for the variables alpha, intercept and outcome. In the second, it will also contain sampled values of p for each of the observed points.