Reference / binference.samplers.Bootstrap

binference.samplers.Bootstrap

class binference.samplers.Bootstrap(model, *, scheme='nonparametric', tol=1e-08, max_iter=5000, rng=None, progress=False)

Bootstrap over objects of the MAP / maximum-likelihood estimator.

scheme="nonparametric" resamples objects with replacement; scheme="bayesian" reweights them with Dirichlet weights (Rubin 1981). Integer weights are multiplicities, so a row of weight w stands for w objects that are resampled individually. For rate models the number of objects is itself random: objects get Poisson(1) counts (or Gamma(1) weights), so the total rate varies between replicates. Every replicate is fitted from an interior starting point; info["n_not_converged"] counts the replicates whose fit did not converge (a warning names it).

The result is the sampling distribution of an estimator, not a posterior: it is flagged approximate=True. Useful for frequentist intervals and comparisons with unfolding / empirical-Bayes practice.

Parameters:

modelPopulationModel

scheme{“nonparametric”, “bayesian”}, optional

Resample objects ("nonparametric") or reweight them with Dirichlet weights ("bayesian").

tolfloat, optional

KKT tolerance of each replicate’s MAP fit.

max_iterint, optional

Most iterations of each replicate’s MAP fit.

rngint, Generator or None

Random number generator.

progressbool, optional

Show a progress bar.

__init__(model, *, scheme='nonparametric', tol=1e-08, max_iter=5000, rng=None, progress=False)

Methods

__init__(model, *[, scheme, tol, max_iter, ...])
run([n_samples])	Return n_samples bootstrap replicates of the estimate.
unsupported(model)	Why the engine cannot run on model, or None when it can.

Attributes

approximate
joint	whether the engine infers a selection from injections or a tallied response jointly (DECISIONS #30, #80, #81)
markov	whether its draws are Markov chains, described by R-hat and ESS
name

run(n_samples=200)

Return n_samples bootstrap replicates of the estimate.

Parameters:

n_samplesint, optional

Number of bootstrap replicates.