29 нояб. 2021 г. · “Flat” priors are effectively saying that you think an effect size >100 on the log scale is just as likely as an effect size of zero if not more ...

20 сент. 2021 г. · The “flat” prior is a (sometimes improper) uniform distribution over the declared bounds of the parameters. Informally, p(\theta) = 1, -\infty<\ ...

set_prior is used to define prior distributions for parameters in brms models. The functions prior, prior_, and prior_string are aliases of set_prior. Arguments · Details

The default prior for population-level effects (including monotonic and category specific effects) is an improper flat prior over the reals. Other common ...

For our model, the first two default priors are (flat) , i.e. uniform distributions (all values are equally probable). The other two priors are Student-t ...

The default prior for autocorrelation parameters is an improper flat prior over the reals. Other priors can be defined with set_prior(" ", class = "ar") or ...

18 мар. 2024 г. · Flat and super-vague priors are not usually recommended and some thought should included to have at least weakly informative priors.

Bayesian models require priors for all parameters. The function brms::prior_summary shows which priors a model fitted with brms has (implicitly) assumed.

6 нояб. 2022 г. · There are different philosophies about priors. The default, flat prior in brms can work in some cases. Many textbooks recommends weakly ...

12 окт. 2016 г. · To most closely reproduce [g]lm[er]() output, users now have to specify flat priors to replace the default. It might be best to, by default, ...