# Automatic imputation - array dimension problem

**URL:** <https://discourse.pymc.io/t/automatic-imputation-array-dimension-problem/8724>\
**Category:** Questions\
**Created:** [February 4, 2022, 2:20pm UTC](https://discourse.pymc.io/t/automatic-imputation-array-dimension-problem/8724 "2022-02-04T14:20:32Z")\
**Posts on this page:** 1\
**Showing post:** 2

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**Author:** ![ckrapu](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/ckrapu/32/1366_2.png) [@ckrapu](https://discourse.pymc.io/u/ckrapu)\
**Post date:** [February 10, 2022, 12:45pm UTC](https://discourse.pymc.io/t/automatic-imputation-array-dimension-problem/8724/2 "2022-02-10T12:45:38Z")

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I recommend reviewing the discussion at these threads:

> [@Multivariate normal with missing data imputation operands could not be broadcast together with shapes (29,2) () (29,)](https://discourse.pymc.io/t/multivariate-normal-with-missing-data-imputation-operands-could-not-be-broadcast-together-with-shapes-29-2-29/5705/14):
>
> Try: pip install -U git+https://github.com/arviz-devs/arviz.git pip install -U git+https://github.com/pymc-devs/pymc3.git

> [@How to marginalize on a Multivariate Normal distribution](https://discourse.pymc.io/t/how-to-marginalize-on-a-multivariate-normal-distribution/2265):
>
> I am new to both probabilistic modeling and PyMC so please bear with me. I have a model that learns a Multivariate normal distribution and now I’d like to find the probabilities over a partial test input. My problem is very similar to the one described in this [question](https://discourse.pymc.io/t/oos-predictions-with-missing-input-values/423). Specifically, I refer to the final comment quoted as below: you need to work out the conditional probability of ma. by marginalizing the MvNormal using [.5, 1.2] and cov, and generate from this new conditional probability (al…

In particular, the first thread recommends using `trace = pm.sample(return_inferencedata=False, compute_convergence_checks=False)` so that the postprocessing doesn’t execute.

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