abc_calibrate

calibrate.abc_calibrate(
    simulator,
    priors,
    observed,
    *,
    population_size=200,
    max_populations=8,
    min_epsilon=0.0,
    n_posterior=None,
    seed=None,
    db_path=None,
)

Calibrate model parameters to observed targets with ABC-SMC.

Parameters

Name	Type	Description	Default
simulator	TargetSimulator	Maps a parameter dict to simulated calibration targets (same keys as observed).	required
priors	Mapping[str, Distribution | Dirichlet]	Parameter priors as heormodel distribution specs.	required
observed	Mapping[str, float]	Observed calibration target values.	required
population_size	int	Particles per ABC-SMC population.	200
max_populations	int	Maximum number of populations.	8
min_epsilon	float	Stop once the acceptance threshold reaches this value.	0.0
n_posterior	int | None	Rows in the returned equally-weighted posterior matrix (default: the final population size).	None
seed	int | None	Seed for the weighted-to-equal resampling step. (The ABC run itself uses pyabc’s internal randomness.)	None
db_path	str | Path | None	Where to store pyabc’s bookkeeping database (default: a temporary file).	None

Returns

Name	Type	Description
	CalibrationResult	A CalibrationResult whose posterior plugs directly into
	CalibrationResult	the probabilistic analysis.

Example

from heormodel.calibrate import abc_calibrate # doctest: +SKIP from heormodel.params import Uniform result = abc_calibrate( # doctest: +SKIP … simulator=lambda p: {“prevalence”: p[“risk”] * 0.5}, … priors={“risk”: Uniform(0.0, 1.0)}, … observed={“prevalence”: 0.15}, … )