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Overview

Project website jasp-stats.org
Project maintainers User avatar JASPStats
Translation license GNU Affero General Public License v3.0 or later
Translation process
  • Translations can be made directly.
  • Translation suggestions can be made.
  • Any authenticated user can contribute.
  • The translation uses bilingual files.
Source code repository https://github.com/jasp-stats/jaspLearnBayes
Repository branch master
Last remote commit Remove QML version in import statements 3645965
User avatar JASPStats authored 2 days ago
Last commit in Weblate Translated using Weblate (Tamil) ac83fcf
User avatar TamilNeram authored 2 weeks ago
Weblate repository https://hosted.weblate.org/git/jasp/jasplearnbayes-r/
File mask po/R-*.po
Translation file Download po/R-es.po
Last change Jan. 18, 2025, 3:51 a.m.
Last change made by None
Language Spanish
Language code es
Text direction Left to right
Number of speakers 506,540,366
Number of plurals 2
Plural type One/other
Plurals Singular 1
Plural 0, 2, 3, 4, 5, 6, 7, 8, 9, 10, …
Plural formula n != 1
4 hours ago

String statistics

Strings percent Hosted strings Words percent Hosted words Characters percent Hosted characters
Total 307 4,453 31,122
Translated 100% 307 100% 4,453 100% 31,122
Needs editing 0% 0 0% 0 0% 0
Read-only 0% 0 0% 0 0% 0
Failing checks 0% 0 0% 0 0% 0
Strings with suggestions 0% 0 0% 0 0% 0
Untranslated strings 0% 0 0% 0 0% 0
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User avatar None

Resource updated
Parsing of the “po/R-es.po” file was enforced. 5 hours ago
User avatar None

Resource updated
Parsing of the “po/R-es.po” file was enforced. 5 hours ago
User avatar None

Resource updated
The “po/R-es.po” file was changed. a week ago
User avatar None

String updated in the repository
The 'Bayes factor' option can compare the predictive accuracies of the hypotheses to the rest of the hypotheses ('vs. All'), the best hypothesis ('vs. best'), or a specific hypothesis selected in the 'vs.' dropdown (the selected hypothesis correponds to the '0' in BF%1$s%2$s). The nominator and denominator of the Bayes factors can be reversed by choosing the 'BF%2$s%1$s' option (quantifying the evidence in favor of the second hypothesis), or transformed to a log scale by choosing the 'log(BF%1$s%2$s)' option.
La opción 'Factor de Bayes' permite comparar las precisiones predictivas de la hipótesis con las demás hipótesis ('vs. Todo'), con la mejor hipótesis ('vs. Mejor'), o con una hipótesis concreta seleccionada del menú desplegable 'vs.' (la hipótesis escogida se corresponde con el '0' en FB%1$s%2$s). El numerador y denominador de los fFactores de Bayes se pueden invertir escogiendo la opción 'FB%2$s%1$s' (cuantificando la evidencia a favor de la segunda hipótesis), o transformar a la escala logarítmica escogiendo la opción 'log(FB%1$s%2$s)'.
a week ago
User avatar None

String updated in the repository
The Bayes factors 'BF' can be obtained by dividing the posterior odds of two models by their prior odds,
<center>BF%1$s%2$s = ( P(H%1$s|data)/P(H%2$s|data) ) / ( P(H%1$s)/P(H%2$s) ),</center> where 'BF%1$s%2$s' stands for the Bayes factor in favor of the 1st model in comparison to the 2nd model (selected from the 'vs.' dropdown menu) and can be interpreted as a natural measure of relative predictive performance. It is also possible to compute a Bayes factor that compares the hypothesis to the remaining hypotheses 'vs. all' exchange the second hypothesis for the sum of the remaining hypotheses, which can be simplified into,
<center>BF%1$s%2$s = ( P(H%1$s|data)/(1-P(H%1$s|data)) ) / ( P(H%1$s)/(1-P(H%1$s)) ).</center>The numerator and denominator of the Bayes factors can be reversed by choosing the 'BF%2$s%1$s' option (quantifying the evidence in favor of the second hypothesis), or transformed to a log scale by choosing the 'log(BF%1$s%2$s)' option.
Los fFactores de Bayes 'FB' pueden obtenerse dividiendo las probabilidades posteriores de dos modelos por sus probabilidades previas,
<center>FB%1$s%2$s = ( P(H%1$s|datos)/P(H%2$s|datos) ) / ( P(H%1$s)/P(H%2$s) ),</center> donde 'FB%1$s%2$s' representa el fFactor de Bayes a favor del modelo 1 en comparación con el modelo 2 (seleccionado del menú desplegable 'vs.') y puede interpretarse como una medida natural del comportamiento predictivo relativo. Para calcular un fFactor de Bayes que compare la hipótesis con las demás hipótesis 'vs. Todo', también se puede cambiar la segunda hipótesis por la suma de las demás hipótesis, lo que se puede simplificar como,
<center>FB%1$s%2$s = ( P(H%1$s|datos)/(1-P(H%1$s|datos)) ) / ( P(H%1$s)/(1-P(H%1$s)) ).</center>El numerador y denominador de los fFactores de Bayes se pueden invertir seleccionando la opción 'FB%2$s%1$s' (cuantificando la evidencia a favor de la segunda hipótesis), o transformar a la escala logarítmica escogiendo la opción 'log(FB%1$s%2$s)'.
a week ago
User avatar None

String updated in the repository
Plotting not possible: One of the Bayes factor is equal to infinity.
No es posible hacer el gráfico: Uuno de los fFactores de Bayes es igual a infinito.
a week ago
User avatar None

String updated in the repository
Bayes factors and posterior probabilities could not be computed since the marginal likelihoods are not available for some models.
Los fFactores de Bayes y las probabilidades posteriores no se pueden calcular, pues las verosimilitudes marginales no están disponibles para algunos modelos.
a week ago
User avatar None

Resource updated
The “po/R-es.po” file was changed. a week ago
User avatar None

String updated in the repository
The 'Bayes factor' option can compare the predictive accuracies of the hypotheses to the rest of the hypotheses ('vs. All'), the best hypothesis ('vs. best'), or a specific hypothesis selected in the 'vs.' dropdown (the selected hypothesis correponds to the '0' in BF%1$s%2$s). The nominator and denominator of the Bayes factors can be reversed by choosing the 'BF%2$s%1$s' option (quantifying the evidence in favor of the second hypothesis), or transformed to a log scale by choosing the 'log(BF%1$s%2$s)' option.
La opción 'Factor de Bayes' permite comparar las precisiones predictivas de la hipótesis con las demás hipótesis ('vs. Todo'), con la mejor hipótesis ('vs. Mejor'), o con una hipótesis concreta seleccionada del menú desplegable 'vs.' (la hipótesis escogida se corresponde con el '0' en FB%1$s%2$s). El numerador y denominador de los Ffactores de Bayes se pueden invertir escogiendo la opción 'FB%2$s%1$s' (cuantificando la evidencia a favor de la segunda hipótesis), o transformar a la escala logarítmica escogiendo la opción 'log(FB%1$s%2$s)'.
a week ago
User avatar None

String updated in the repository
The Bayes factors 'BF' can be obtained by dividing the posterior odds of two models by their prior odds,
<center>BF%1$s%2$s = ( P(H%1$s|data)/P(H%2$s|data) ) / ( P(H%1$s)/P(H%2$s) ),</center> where 'BF%1$s%2$s' stands for the Bayes factor in favor of the 1st model in comparison to the 2nd model (selected from the 'vs.' dropdown menu) and can be interpreted as a natural measure of relative predictive performance. It is also possible to compute a Bayes factor that compares the hypothesis to the remaining hypotheses 'vs. all' exchange the second hypothesis for the sum of the remaining hypotheses, which can be simplified into,
<center>BF%1$s%2$s = ( P(H%1$s|data)/(1-P(H%1$s|data)) ) / ( P(H%1$s)/(1-P(H%1$s)) ).</center>The numerator and denominator of the Bayes factors can be reversed by choosing the 'BF%2$s%1$s' option (quantifying the evidence in favor of the second hypothesis), or transformed to a log scale by choosing the 'log(BF%1$s%2$s)' option.
Los Ffactores de Bayes 'FB' pueden obtenerse dividiendo las probabilidades posteriores de dos modelos por sus probabilidades previas,
<center>FB%1$s%2$s = ( P(H%1$s|datos)/P(H%2$s|datos) ) / ( P(H%1$s)/P(H%2$s) ),</center> donde 'FB%1$s%2$s' representa el Ffactor de Bayes a favor del modelo 1 en comparación con el modelo 2 (seleccionado del menú desplegable 'vs.') y puede interpretarse como una medida natural del comportamiento predictivo relativo. Para calcular un Ffactor de Bayes que compare la hipótesis con las demás hipótesis 'vs. Todo', también se puede cambiar la segunda hipótesis por la suma de las demás hipótesis, lo que se puede simplificar como,
<center>FB%1$s%2$s = ( P(H%1$s|datos)/(1-P(H%1$s|datos)) ) / ( P(H%1$s)/(1-P(H%1$s)) ).</center>El numerador y denominador de los Ffactores de Bayes se pueden invertir seleccionando la opción 'FB%2$s%1$s' (cuantificando la evidencia a favor de la segunda hipótesis), o transformar a la escala logarítmica escogiendo la opción 'log(FB%1$s%2$s)'.
a week ago
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