# Correlate different process

**URL:** <https://discourse.pymc.io/t/correlate-different-process/11609>\
**Category:** General\
**Created:** [March 15, 2023, 3:21pm UTC](https://discourse.pymc.io/t/correlate-different-process/11609 "2023-03-15T15:21:21Z")\
**Posts on this page:** 3\
**Page:** 1

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**Author:** ![nipnipj](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/nipnipj/32/2572_2.png) [@nipnipj](https://discourse.pymc.io/u/nipnipj)\
**Post date:** [March 15, 2023, 3:21pm UTC](https://discourse.pymc.io/t/correlate-different-process/11609/1 "2023-03-15T15:21:21Z")

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Let’s say we need to use 1 regression equation (Linear regression) and 1 classification (probit classification). Is it possible to correlated errors of these 2 different models. I was thinking on a multivariate normal, but it might not be the best idea.

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**Author:** ![ricardoV94](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/ricardov94/32/5775_2.png) [@ricardoV94](https://discourse.pymc.io/u/ricardoV94)\
**Post date:** [March 15, 2023, 4:57pm UTC](https://discourse.pymc.io/t/correlate-different-process/11609/2 "2023-03-15T16:57:07Z")

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This WIP notebook may give you other ideas: [Bayesian copula estimation example notebook by drbenvincent · Pull Request #257 · pymc-devs/pymc-examples · GitHub](https://github.com/pymc-devs/pymc-examples/pull/257)

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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 16, 2023, 1:28am UTC](https://discourse.pymc.io/t/correlate-different-process/11609/3 "2023-03-16T01:28:00Z")

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Without commenting on the method itself, nothing stops you from stacking the means of two GLMs, adding a covariance matrix, and estimating them jointly as a Multivariate Normal. Seems like in the worst case you will just get a diagonal covariance matrix (ok ok the real worst case is infinity divergences)