pymc.compute_deterministics#

pymc.compute_deterministics(dataset, *, var_names=None, model=None, sample_dims=('chain', 'draw'), merge_dataset=False, progressbar=True, compile_kwargs=None)[source]#

Compute model deterministics given a dataset with values for model variables.

Parameters:

datasetDataset: Dataset with values for model variables. Commonly InferenceData[“posterior”].
var_namessequence of str, optional: List of names of deterministic variable to compute. If None, compute all deterministics in the model.
modelModel, optional: Model to use. If None, use context model.
sample_dimssequence of str, default (“chain”, “draw”): Sample (batch) dimensions of the dataset over which to compute the deterministics.
merge_datasetbool, default False: Whether to extend the original dataset or return a new one.
progressbarbool, default True: Whether to display a progress bar in the command line.
progressbar_themeTheme, optional: Custom theme for the progress bar.
compile_kwargs: dict, optional: Additional arguments passed to model.compile_fn.

Returns:

Dataset: Dataset with values for the deterministics.

Examples

import pymc as pm

with pm.Model(coords={"group": (0, 2, 4)}) as m:
    mu_raw = pm.Normal("mu_raw", 0, 1, dims="group")
    mu = pm.Deterministic("mu", mu_raw.cumsum(), dims="group")

    trace = pm.sample(var_names=["mu_raw"], chains=2, tune=5 draws=5)

assert "mu" not in trace.posterior

with m:
    trace.posterior = pm.compute_deterministics(trace.posterior, merge_dataset=True)

assert "mu" in trace.posterior