histogram_approximation#

pymc_experimental.distributions.histogram_approximation(name, dist, *, observed, **h_kwargs)[source]#

Approximate a distribution with a histogram potential.

Parameters:

  • name (str) – Name for the Potential

  • dist (TensorVariable) – The output of pm.Distribution.dist()

  • observed (ArrayLike) – Observed value to construct a histogram. Histogram is computed over 0th axis. Dask is supported.

Returns:

Potential

Return type:

TensorVariable

Examples

Discrete variables are reduced to unique repetitions (up to min_count)

>>> import pymc as pm
>>> import pymc_experimental as pmx
>>> production = np.random.poisson([1, 2, 5], size=(1000, 3))
>>> with pm.Model(coords=dict(plant=range(3))):
...     lam = pm.Exponential("lam", 1.0, dims="plant")
...     pot = pmx.distributions.histogram_approximation(
...         "pot", pm.Poisson.dist(lam), observed=production, min_count=2
...     )

Continuous variables are discretized into n_quantiles

>>> measurements = np.random.normal([1, 2, 3], [0.1, 0.4, 0.2], size=(10000, 3))
>>> with pm.Model(coords=dict(tests=range(3))):
...     m = pm.Normal("m", dims="tests")
...     s = pm.LogNormal("s", dims="tests")
...     pot = pmx.distributions.histogram_approximation(
...         "pot", pm.Normal.dist(m, s),
...         observed=measurements, n_quantiles=50
...     )

For special cases like Zero Inflation in Continuous variables there is a flag. The flag adds a separate bin for zeros

>>> measurements = abs(measurements)
>>> measurements[100:] = 0
>>> with pm.Model(coords=dict(tests=range(3))):
...     m = pm.Normal("m", dims="tests")
...     s = pm.LogNormal("s", dims="tests")
...     pot = pmx.distributions.histogram_approximation(
...         "pot", pm.Normal.dist(m, s),
...         observed=measurements, n_quantiles=50, zero_inflation=True
...     )