From c27036be50cb2b6dbf1809b8041e70c2dd5b712a Mon Sep 17 00:00:00 2001 From: Nathan Simpson Date: Mon, 4 Apr 2022 20:17:26 +0200 Subject: [PATCH] Update README.md --- README.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index e5d14aa..0e616cf 100644 --- a/README.md +++ b/README.md @@ -38,17 +38,17 @@ Based on [`jax`](http://github.com/google/jax). Where possible, function APIs tr ## currently implemented: - **basic operations**: - - [`relaxed.hist`](src/relaxed/ops/histograms.py): histograms via kernel density estimation (tunable bandwidth) - - [`relaxed.cut`](src/relaxed/ops/cuts.py): approximates a hard cut with a sigmoid function (tunable slope) + - [`relaxed.hist`](src/relaxed/ops/histograms.py): histograms via kernel density estimation (tunable bandwidth). + - [`relaxed.cut`](src/relaxed/ops/cuts.py): approximates a hard cut with a sigmoid function (tunable slope). - **fitting routines**: - - [`relaxed.mle.fit`](src/relaxed/mle/global_fit.py): global MLE fit - - [`relaxed.mle.fixed_poi_fit`](src/relaxed/infer/hypothesis_test.py): constrained fit given a value of a parameter of interest + - [`relaxed.mle.fit`](src/relaxed/mle/global_fit.py): global MLE fit. + - [`relaxed.mle.fixed_poi_fit`](src/relaxed/infer/hypothesis_test.py): constrained fit given a value of a parameter of interest. - **inference**: - - [`relaxed.infer.hypotest`](src/relaxed/infer/hypothesis_test.py): hypothesis test using the profile likelihood as a test statistic - - [`relaxed.fisher_info`](src/relaxed/ops/fisher_information.py): the fisher information matrix (of a `pyhf`-type model) - - [`relaxed.cramer_rao_uncert`](src/relaxed/ops/fisher_information.py): inverts the fisher information matrix to provide uncertainties valid through the [Cramér-Rao bound](https://en.wikipedia.org/wiki/Cram%C3%A9r%E2%80%93Rao_bound) + - [`relaxed.infer.hypotest`](src/relaxed/infer/hypothesis_test.py): hypothesis test based on the profile likelihood. Supports test statistics for both limit setting (`q`) and discovery (`q_0`). + - [`relaxed.fisher_info`](src/relaxed/ops/fisher_information.py): the fisher information matrix (of a `pyhf`-type model). + - [`relaxed.cramer_rao_uncert`](src/relaxed/ops/fisher_information.py): inverts the fisher information matrix to provide uncertainties valid through the [Cramér-Rao bound](https://en.wikipedia.org/wiki/Cram%C3%A9r%E2%80%93Rao_bound). - **metrics**: - - [`relaxed.metrics.gaussianity`](src/relaxed/metrics/likelihood_gaussianity.py): an experimental metric that quantifies the mean-squared difference of a likelihood function with respect to its gaussian approximation (covariance calculated using the Cramér-Rao bound above) + - [`relaxed.metrics.gaussianity`](src/relaxed/metrics/likelihood_gaussianity.py): an experimental metric that quantifies the mean-squared difference of a likelihood function with respect to its gaussian approximation (covariance calculated using the Cramér-Rao bound above). - [`relaxed.metrics.asimov_sig`](src/relaxed/metrics/significance.py): easy access to the (single- and multi-bin) stat-only expected significance. We're maintaining a list of desired differentiable operations in [`list_of_operations.md`](list_of_operations.md) (thanks to [@cranmer](http://github.com/cranmer)) -- feel free to take inspiration or contribute with a PR if there's one you can handle :)