scipy-yli/tests/test_hdi.py

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# scipy-yli: Helpful SciPy utilities and recipes
# Copyright © 2022 Lee Yingtong Li (RunasSudo)
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
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from pytest import approx
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import numpy as np
from scipy import stats
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import yli
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def test_hdi_beta_vs_r():
"""Compare yli.hdi for beta distribution with R binom.bayes"""
# Inference on a beta-binomial posterior
n, N = 12, 250
prior_a, prior_b = 1, 1
distribution = stats.beta(n + prior_a, N - n + prior_b)
# Compute HDI
hdi = yli.hdi(distribution, 0.95)
#> library(binom)
#> binom.bayes(12, 250, conf.level=0.95, type='highest', prior.shape1=1, prior.shape2=1)
# method x n shape1 shape2 mean lower upper sig
#1 bayes 12 250 13 239 0.0515873 0.02594348 0.0793006 0.05
expected = np.array([0.02594348, 0.0793006])
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assert (hdi.lower, hdi.upper) == approx(expected)