Markov Combinations of Discrete Statistical Models

Fuente: arXiv
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Autori principali: Marigliano, Orlando, Riccomagno, Eva
Natura: Preprint
Pubblicazione: 2025
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author Marigliano, Orlando
Riccomagno, Eva
author_facet Marigliano, Orlando
Riccomagno, Eva
contents Markov combination is an operation that takes two statistical models and produces a third whose marginal distributions include those of the original models. Building upon and extending existing work in the Gaussian case, we develop Markov combinations for categorical variables and their statistical models. We present several variants of this operation, both algorithmically and from a sampling perspective, and discuss relevant examples and theoretical properties. We describe Markov combinations for special models such as regular exponential families, discrete copulas, and staged trees. Finally, we offer results about model invariance and the maximum likelihood estimation of Markov combinations.
format Preprint
id arxiv_https___arxiv_org_abs_2509_18983
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Markov Combinations of Discrete Statistical Models
Marigliano, Orlando
Riccomagno, Eva
Statistics Theory
Methodology
62E10, 62E15 (Primary) 68R99, 62H99, 62R01 (Secondary)
Markov combination is an operation that takes two statistical models and produces a third whose marginal distributions include those of the original models. Building upon and extending existing work in the Gaussian case, we develop Markov combinations for categorical variables and their statistical models. We present several variants of this operation, both algorithmically and from a sampling perspective, and discuss relevant examples and theoretical properties. We describe Markov combinations for special models such as regular exponential families, discrete copulas, and staged trees. Finally, we offer results about model invariance and the maximum likelihood estimation of Markov combinations.
title Markov Combinations of Discrete Statistical Models
topic Statistics Theory
Methodology
62E10, 62E15 (Primary) 68R99, 62H99, 62R01 (Secondary)
url https://arxiv.org/abs/2509.18983