On equal-input and monotone Markov matrices

Baake M, Sumner J (2022)
Advances in Applied Probability 54(2): 460-492.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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DOI
Autor*in
Baake, MichaelUniBi; Sumner, Jeremy
Einrichtung
Fakultät für Mathematik
Abstract / Bemerkung
The practically important classes of equal-input and of monotone Markov matrices are revisited, with special focus on embeddability, infinite divisibility, and mutual relations. Several uniqueness results for the classic Markov embedding problem are obtained in the process. To achieve our results, we need to employ various algebraic and geometric tools, including commutativity, permutation invariance, and convexity. Of particular relevance in several demarcation results are Markov matrices that are idempotents.
Stichworte
Markov matrix; embedding problem; monotone partial order; convexity
Erscheinungsjahr
2022
Zeitschriftentitel
Advances in Applied Probability
Band
54
Ausgabe
2
Seite(n)
460-492
ISSN
0001-8678
eISSN
1475-6064
Page URI
https://pub.uni-bielefeld.de/record/2964347

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Baake M, Sumner J. On equal-input and monotone Markov matrices. Advances in Applied Probability. 2022;54(2):460-492.
Baake, M., & Sumner, J. (2022). On equal-input and monotone Markov matrices. Advances in Applied Probability, 54(2), 460-492. https://doi.org/10.1017/apr.2021.39
Baake, Michael, and Sumner, Jeremy. 2022. “On equal-input and monotone Markov matrices”. Advances in Applied Probability 54 (2): 460-492.
Baake, M., and Sumner, J. (2022). On equal-input and monotone Markov matrices. Advances in Applied Probability 54, 460-492.
Baake, M., & Sumner, J., 2022. On equal-input and monotone Markov matrices. Advances in Applied Probability, 54(2), p 460-492.
M. Baake and J. Sumner, “On equal-input and monotone Markov matrices”, Advances in Applied Probability, vol. 54, 2022, pp. 460-492.
Baake, M., Sumner, J.: On equal-input and monotone Markov matrices. Advances in Applied Probability. 54, 460-492 (2022).
Baake, Michael, and Sumner, Jeremy. “On equal-input and monotone Markov matrices”. Advances in Applied Probability 54.2 (2022): 460-492.
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