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2026
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| Online Access: | https://doi.org/10.5281/zenodo.20209402 |
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| _version_ | 1866901713247535104 |
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| author | Ibarra Mejia, David Camilo |
| author_facet | Ibarra Mejia, David Camilo |
| contents | <p>**v3 — Major Revision**</p> <div> <div>A paragraph was added proposing a stochastic reformulation of the Omega Equation as a direction for future work, aligned with the approach of Prantzos (2013) for the Drake Equation. In such a reformulation, each factor would be drawn from a probability distribution (e.g., Cm ~ LogUniform, Ai ~ LogUniform, D ~ Uniform) with a positive covariance matrix reflecting the expected cov(Cm, Ai) > 0 and cov(Ai, D) > 0. A Monte Carlo propagation across a large number of draws would yield a posterior distribution over Ω_MW</div> </div> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20209402 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Omega Equation vs Drake Equation Ibarra Mejia, David Camilo <p>**v3 — Major Revision**</p> <div> <div>A paragraph was added proposing a stochastic reformulation of the Omega Equation as a direction for future work, aligned with the approach of Prantzos (2013) for the Drake Equation. In such a reformulation, each factor would be drawn from a probability distribution (e.g., Cm ~ LogUniform, Ai ~ LogUniform, D ~ Uniform) with a positive covariance matrix reflecting the expected cov(Cm, Ai) > 0 and cov(Ai, D) > 0. A Monte Carlo propagation across a large number of draws would yield a posterior distribution over Ω_MW</div> </div> |
| title | Omega Equation vs Drake Equation |
| url | https://doi.org/10.5281/zenodo.20209402 |