# State of the Art samplers? Computational Lebesgue Integration techniques?

**URL:** https://discourse.pymc.io/t/state-of-the-art-samplers-computational-lebesgue-integration-techniques/16166
**Category:** version agnostic
**Created:** [November 22, 2024, 7:43pm UTC](https://discourse.pymc.io/t/state-of-the-art-samplers-computational-lebesgue-integration-techniques/16166 "2024-11-22T19:43:21Z")
**Posts on this page:** 1
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### Author: ![cluhmann](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/cluhmann/32/3083_2.png) [@cluhmann](https://discourse.pymc.io/u/cluhmann)
#### Post date: [November 22, 2024, 7:51pm UTC](https://discourse.pymc.io/t/state-of-the-art-samplers-computational-lebesgue-integration-techniques/16166/2 "2024-11-22T19:51:14Z")

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This has come up a few times (e.g., [here](https://discourse.pymc.io/t/how-does-pymc-handle-the-curse-of-dimensionality/13729) and [here](https://discourse.pymc.io/t/chainsail-a-web-service-for-sampling-multimodal-distributions-opinions-and-beta-testers-wanted/10115/3)). My understanding of parallel tempering/replica exchange is that it is particularly useful for multi modal posteriors rather than a generic approach to sampling (but could be very wrong). Performance of samplers is often difficult to compare, but there is obvious interest in any performance gains that may be available.

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