# Bivariate Poisson

**URL:** <https://discourse.pymc.io/t/bivariate-poisson/10843>\
**Category:** v5\
**Tags:** modeling\
**Created:** [November 17, 2022, 10:48am UTC](https://discourse.pymc.io/t/bivariate-poisson/10843 "2022-11-17T10:48:51Z")\
**Posts on this page:** 1\
**Showing post:** 4

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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:** [December 2, 2022, 6:08pm UTC](https://discourse.pymc.io/t/bivariate-poisson/10843/4 "2022-12-02T18:08:20Z")

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Here’s [an example notebook](https://colab.research.google.com/drive/1tBkSzDc58KP42enCXtQZuxpbCihAYmSI#scrollTo=AD3FbUhaV12R) with a few ways to implement the correlated Poisson model. There’s more discussion on this thread which is essentially the same topic:

> [@Dependent histogram count data](https://discourse.pymc.io/t/dependent-histogram-count-data/10872):
>
> Hey! This is perhaps a broad question not limited specifically to PyMC, but I figured this is a good place to ask slight_smile My input data is standard (as in, some vector of of covariates for each sample), but my observations are less so. In fact, my observations are a matrix (a vector for each sample) with histogram count data. Specifically, for each sample, the target is a vector of counts for each bin, which are of course somewhat correlated: index | X | bin\_0 | bin\_1 | bin\_2 | ... | …

There’s a lot to unpack in that example notebook - feel free to call out specific parts that you’d like explained.

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_[View the full topic](https://discourse.pymc.io/t/bivariate-poisson/10843)._