2012
DOI: 10.15633/acr.14
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Cyberprzestrzeń i mass media sprzymierzeńcem duchowości?

Abstract: Zasygnalizowany w tytule temat jest bardzo obszerny, dotyczy przecież rzeczywistości złożonych, bogatych w treści. Zarówno duchowość chrześcijańska jak i cyberprzestrzeń oraz mass media odgrywają bardzo znaczącą rolę w życiu człowieka, warunkując na przykład jego postępowanie i losy. Stąd wskazanej problematyce warto poświęcić uwagę, nawet jeśli mamy do czynienia z przyczynkiem do jej opracowania, do bardziej owocnego spotkania między duchowością oraz cyberprzestrzenią i mediami.

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“…GLMM, as a model with random effects, can handle the analysis of complex pedigrees of varying size and structure, while also accounting for measurement errors and missing values 24. To obtain estimates for GLMM, we used a binary threshold-linear mixed model in a Bayesian framework with a non-informative prior to estimate the proportions of phenotypic variance explained by direct additive genetics and maternal effect (genetic maternal effect and environmental maternal effect) 27. We applied a Gibbs sampler implemented in thrgibbs1f90b27 and generated a sample size of 150 000, with 50 000 burn-in, from the posterior distribution of the variance components.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…GLMM, as a model with random effects, can handle the analysis of complex pedigrees of varying size and structure, while also accounting for measurement errors and missing values 24. To obtain estimates for GLMM, we used a binary threshold-linear mixed model in a Bayesian framework with a non-informative prior to estimate the proportions of phenotypic variance explained by direct additive genetics and maternal effect (genetic maternal effect and environmental maternal effect) 27. We applied a Gibbs sampler implemented in thrgibbs1f90b27 and generated a sample size of 150 000, with 50 000 burn-in, from the posterior distribution of the variance components.…”
Section: Methodsmentioning
confidence: 99%
“…To obtain estimates for GLMM, we used a binary threshold-linear mixed model in a Bayesian framework with a non-informative prior to estimate the proportions of phenotypic variance explained by direct additive genetics and maternal effect (genetic maternal effect and environmental maternal effect) 27. We applied a Gibbs sampler implemented in thrgibbs1f90b27 and generated a sample size of 150 000, with 50 000 burn-in, from the posterior distribution of the variance components. Then, we calculated the posterior mean as an estimate of the variance components.…”
Section: Methodsmentioning
confidence: 99%