# Recreating Normal Distrbution using logp - sampling diverges

**URL:** <https://discourse.pymc.io/t/recreating-normal-distrbution-using-logp-sampling-diverges/13937>\
**Category:** General\
**Tags:** modeling\
**Created:** [March 5, 2024, 5:44am UTC](https://discourse.pymc.io/t/recreating-normal-distrbution-using-logp-sampling-diverges/13937 "2024-03-05T05:44:30Z")\
**Posts on this page:** 1\
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**Author:** ![jessegrabowski](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/jessegrabowski/32/5010_2.png) [@jessegrabowski](https://discourse.pymc.io/u/jessegrabowski)\
**Post date:** [March 5, 2024, 6:02am UTC](https://discourse.pymc.io/t/recreating-normal-distrbution-using-logp-sampling-diverges/13937/2 "2024-03-05T06:02:28Z")

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There’s no observed argument in model1, is that a typo? When I add it everything runs fine.

In general, it can be more difficult to sample from priors using MCMC than from posteriors. See [this discussion](https://discourse.pymc.io/t/why-does-mcmc-without-observations-have-difficulty-sampling/12809/4) for details.

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