# How do I evaluate my model, in terms of bias, coverage, etc.?

**URL:** <https://discourse.mc-stan.org/t/how-do-i-evaluate-my-model-in-terms-of-bias-coverage-etc/29450>\
**Category:** General\
**Tags:** simulation-based-calibration\
**Created:** [November 12, 2022, 12:06pm UTC](https://discourse.mc-stan.org/t/how-do-i-evaluate-my-model-in-terms-of-bias-coverage-etc/29450 "2022-11-12T12:06:02Z")\
**Posts on this page:** 1\
**Showing post:** 18

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**Author:** ![andrewgelman](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/andrewgelman/32/1577_2.png) [@andrewgelman](https://discourse.mc-stan.org/u/andrewgelman)\
**Post date:** [January 4, 2023, 8:38am UTC](https://discourse.mc-stan.org/t/how-do-i-evaluate-my-model-in-terms-of-bias-coverage-etc/29450/18 "2023-01-04T08:38:00Z")

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In particular, this new paper by Martin Modrák, Angie Moon, Shinyoung Kim, Paul Bürkner, Niko Huurre, Kateřina Faltejsková, Aki Vehtari, and myself, which Martin discussed on the forum here: [New preprint on model/algorithm validation with SBC - It is stronger than you thought!](https://discourse.mc-stan.org/t/new-preprint-on-model-algorithm-validation-with-sbc-it-is-stronger-than-you-thought/29372)

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