Simulation-Based Calibration Checking for Bayesian Computation: The Choice of Test Quantities Shapes Sensitivity

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61388963%3A_____%2F25%3A00636655" target="_blank" >RIV/61388963:_____/25:00636655 - isvavai.cz</a>

Alternative codes found

RIV/61388971:_____/25:00636655

Result on the web

<a href="https://projecteuclid.org/journals/bayesian-analysis/volume-20/issue-2/Simulation-Based-Calibration-Checking-for-Bayesian-Computation--The-Choice/10.1214/23-BA1404.full" target="_blank" >https://projecteuclid.org/journals/bayesian-analysis/volume-20/issue-2/Simulation-Based-Calibration-Checking-for-Bayesian-Computation--The-Choice/10.1214/23-BA1404.full</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1214/23-BA1404" target="_blank" >10.1214/23-BA1404</a>

Result language

angličtina

Original language name

Simulation-Based Calibration Checking for Bayesian Computation: The Choice of Test Quantities Shapes Sensitivity

Original language description

Simulation-based calibration checking (SBC) is a practical method to validate computationally-derived posterior distributions or their approximations. In this paper, we introduce a new variant of SBC to alleviate several known problems. Our variant allows the user to in principle detect any possible issue with the posterior, while previously reported implementations could never detect large classes of problems including when the posterior is equal to the prior. This is made possible by including additional data-dependent test quantities when running SBC. We argue and demonstrate that the joint likelihood of the data is an especially useful test quantity. Some other types of test quantities and their theoretical and practical benefits are also investigated. We provide theoretical analysis of SBC, thereby providing a more complete understanding of the underlying statistical mechanisms. We also bring attention to a relatively common mistake in the literature and clarify the difference between SBC and checks based on the data-averaged posterior. We support our recommendations with numerical case studies on a multivariate normal example and a case study in implementing an ordered simplex data type for use with Hamiltonian Monte Carlo. The SBC variant introduced in this paper is implemented in the SBC R package.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/LM2023055" target="_blank" >LM2023055: Czech National Infrastructure for Biological Data</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Bayesian Analysis

ISSN

1931-6690

e-ISSN

1936-0975

Volume of the periodical

20

Issue of the periodical within the volume

2

Country of publishing house

US - UNITED STATES

Number of pages

28

Pages from-to

461-488

UT code for WoS article

001504675700005

EID of the result in the Scopus database

2-s2.0-105008194200