# A first approach to closeness distributions

arXiv: https://arxiv.org/abs/2111.08357 · HTML: https://mlsanity.com/paper/2111.08357/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 16 Nov 2021.

## Builds on (most cited first, 4 of 4)

- [Tensor Variable Elimination for Plated Factor Graphs](https://mlsanity.com/paper/1902.03210/) (2019)
- [Hierarchical Multinomial-Dirichlet model for the estimation of conditional probability tables](https://mlsanity.com/paper/1708.06935/) (2017)
- [Estimation of COVID-19 spread curves integrating global data and borrowing information](https://mlsanity.com/paper/2005.00662/) (2020)
- [Parametrization invariant interpretation of priors and posteriors](https://mlsanity.com/paper/2105.08304/) (2021)

## Built on by (most cited first, 1 of 1)

- [Analyzing social media with crowdsourcing in Crowd4SDG](https://mlsanity.com/paper/2208.02689/) (2022)

## Abstract (arXiv)

Probabilistic graphical models allow us to encode a large probability distribution as a composition of smaller ones. It is oftentimes the case that we are interested in incorporating in the model the idea that some of these smaller distributions are likely to be similar to one another. In this paper we provide an information geometric approach on how to incorporate this information, and see that it allows us to reinterpret some already existing models.