# Constraints for multivariate prior in Bayesian update scheme

**URL:** <https://discourse.pymc.io/t/constraints-for-multivariate-prior-in-bayesian-update-scheme/15977>\
**Category:** version agnostic\
**Tags:** modeling\
**Created:** [October 16, 2024, 7:56am UTC](https://discourse.pymc.io/t/constraints-for-multivariate-prior-in-bayesian-update-scheme/15977 "2024-10-16T07:56:55Z")\
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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:** [October 16, 2024, 11:30am UTC](https://discourse.pymc.io/t/constraints-for-multivariate-prior-in-bayesian-update-scheme/15977/5 "2024-10-16T11:30:34Z")

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Truncated MvN absolutely exists in principle, but I don’t know if it’s implemented in PyMC. @ricardoV94 ? If it does you could give it a try. It wouldn’t fix your ordering problem, though. See [this discussion](https://discourse.pymc.io/t/how-to-generate-an-increasing-sequence/14794) for some insight into how joint ordering changes marginal distributions.

Can you share any details of your transition/observation equations? My first instinct would be to model the hidden states in an unconstrained latent space, then transform that into the observation space. If you’re working with a particle filter this would essentially be exactly what we do when MCMC sampling.

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