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A First Approach to Closeness Distributions

Author

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  • Jesus Cerquides

    (Instituto de Investigación en Inteligencia Artificial (IIIA-CSIC), Campus UAB, 08193 Cerdanyola, Spain)

Abstract

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. Our proposal relies on providing a formal definition of what it means to be close. We provide an example on how this definition can be actioned for multinomial distributions. We use the results on multinomial distributions to reinterpret two already existing hierarchical models in terms of closeness distributions.

Suggested Citation

  • Jesus Cerquides, 2021. "A First Approach to Closeness Distributions," Mathematics, MDPI, vol. 9(23), pages 1-12, December.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:23:p:3112-:d:693922
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    References listed on IDEAS

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    1. Se Yoon Lee & Bowen Lei & Bani Mallick, 2020. "Estimation of COVID-19 spread curves integrating global data and borrowing information," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-17, July.
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