2018
DOI: 10.1073/pnas.1808578115
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Bayesian approach to SETI

Abstract: The search for technosignatures from hypothetical galactic civilizations is going through a new phase of intense activity. For the first time, a significant fraction of the vast search space is expected to be sampled in the foreseeable future, potentially bringing informative data about the abundance of detectable extraterrestrial civilizations, or the lack thereof. Starting from the current state of ignorance about the galactic population of non-natural electromagnetic signals, we formulate a Bayesian statist… Show more

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Cited by 16 publications
(14 citation statements)
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“…Secondly, one could run Monte Carlo simulations in which transmitters are placed at different distances, and their signal properties are drawn from varied probability distributions, following a Bayesian approach (e.g. Grimaldi & Marcy 2018); this is an avenue for future investigation. Enriquez et.…”
Section: Limits On Narrowband Technosignaturesmentioning
confidence: 99%
“…Secondly, one could run Monte Carlo simulations in which transmitters are placed at different distances, and their signal properties are drawn from varied probability distributions, following a Bayesian approach (e.g. Grimaldi & Marcy 2018); this is an avenue for future investigation. Enriquez et.…”
Section: Limits On Narrowband Technosignaturesmentioning
confidence: 99%
“…Technological processes are bootstrapped to biological ones, and hence, by extension, the production of technosignatures involves processes that necessarily have a biological origin. Examples of biosignatures and technosignatures include chemical products produced by the action of complex molecular systems such as networks of enzymes [ 20 ] and objects whose creation involved any biological organisms such as technological artefacts [ 21 ], complex chemicals made in the laboratory [ 22 ], and the complete works of Shakespeare. Finding the object in some abundance, or a single object with a large number of complex, but precisely repeating features, is required in order to distinguish single random occurrences from deliberately generated objects.…”
Section: Introductionmentioning
confidence: 99%
“…The choice of possible types of probability calculations depends on many factors, which become especially important when dealing with data-rich contexts, cosmological timescales, and sources of uncertainty that lead to significant discrepancies in results. On the other hand, Bayesian probability theory incorporates sources of uncertainty much better into its formulas and considers research information gaps and blind spots much more carefully (Scharf and Cronin, 2016;Grimaldi and Marcy, 2018). Insights from the philosophy and sociology of science come to the fore, especially in this kind of research framework and "almostmetaphysical" contexts.…”
Section: Further Research Guidelinesmentioning
confidence: 99%