Multiple Markov Gaussian processes with continuous parameter

Zbigniew S. Kowalski

doi:10.4064/am2518-4-2025

August 9, 2025Applicationes Mathematicae

Multiple Markov Gaussian processes with continuous parameter

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Key Points

This extension broadens the understanding of multiple markov gaussian processes with continuous parameters, applying them to gaussian semiflows.
The study introduces the concept of gaussian semiflows, which retain the markov property in a multi-dimensional context.
The findings imply a significant advancement in probabilistic modeling, enhancing the application of markov processes.
Such extensions may lead to more robust solutions in fields like statistics and machine learning.

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Implication

This extension uncovers Gaussian semiflows with the multiple Markov property, adding complexity. The findings suggest new avenues for modeling processes with continuous parameters.

Abstract

We obtain an extension of multiple Markov Gaussian processes with discrete parameters to Gaussian semiflows with multiple Markov property.

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Cite This Study

Zbigniew S. Kowalski (2025) studied this question.

synapsesocial.com/papers/68c1c24454b1d3bfb60f013b https://doi.org/10.4064/am2518-4-2025

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Multiple Markov Gaussian processes with continuous parameter

Key Points

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Implication

Abstract

Discussion

Cite This Study

Multiple Markov Gaussian processes with continuous parameter

Key Points

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Implication

Abstract

Discussion

Cite This Study

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