mlsanityData to 7 Oct 2026

A first approach to closeness distributions

arXiv 2111.08357 ↗First posted 16 Nov 2021cs.AI math.ST stat.MLcited by 1

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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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 4 of its 24 references that are in the map. First posted on arXiv 16 Nov 2021.

Builds on

4 of 24 references.

References · 4

  1. 2019Tensor Variable Elimination for Plated Factor Graphscited by 5
  2. 2017Hierarchical Multinomial-Dirichlet model for the estimation of conditional probability tablescited by 2
  3. 2020Estimation of COVID-19 spread curves integrating global data and borrowing informationcited by 2
  4. 2021Parametrization invariant interpretation of priors and posteriorscited by 1

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Citing papers per year: 2022: 1.

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  1. 2022Analyzing social media with crowdsourcing in Crowd4SDGcited by 0