Welcome to tarp
’s documentation!#
tarp
is a small python package for performing statistical coverage tests to assess
the quality of posterior estimators.
tarp
’s small API is documented below.
- tarp.get_drp_coverage(samples, theta, references='random', metric='euclidean')[source]#
Deprecated since version 0.1.0: This will be removed in 0.2.0. Use get_tarp_coverage instead
- Return type:
Tuple
[ndarray
,ndarray
]
- tarp.get_tarp_coverage(samples, theta, references='random', metric='euclidean', num_alpha_bins=None, num_bootstrap=100, norm=False, bootstrap=False, seed=None)[source]#
Estimates coverage with the TARP method.
Reference: Lemos, Coogan et al 2023
- Parameters:
samples (
ndarray
) – the samples to compute the coverage of, with shape(n_samples, n_sims, n_dims)
.theta (
ndarray
) – the true parameter values for each samples, with shape(n_sims, n_dims)
.references (
Union
[str
,ndarray
]) – the reference points to use for the DRP regions, with shape(n_references, n_sims)
, or the string"random"
. If the later, then the reference points are chosen randomly from the unit hypercube over the parameter space.metric (
str
) – the metric to use when computing the distance. Can be"euclidean"
or"manhattan"
.num_alpha_bins (
Optional
[int
]) – number of bins to use for the credibility values. IfNone
, thenn_sims // 10
bins are used.num_bootstrap (
int
) – number of bootstrap iterations to perform (Default = 100)norm (
bool
) – whether to apply or not the normalization (Default = False)bootstrap (
bool
) – whether to use bootstrap to estimate uncertainties (Default = False)seed (
Optional
[int
]) – the seed to use for the random number generator. IfNone
, then no seed
- Return type:
Tuple
[ndarray
,ndarray
]- Returns:
Expected coverage probability (
ecp
) and credibility values (alpha
). If bootstrap is True, the ecp array has an extra dimension corresponding to the number of bootstrap iterations