Thank you so much for building bmrs. You saved my master thesis, got me into Bayesian statistics and made me learn R, which is now a staple tool of my professional career.
Very valuable package indeed! I'm wondering how to model the covariance structure in a bayesian longitudinal setting, similar to covariance patterns such as compound symmetry, autoregressive, Topelitz etc. in the frequentist world. In the frequentist world, taking serial correlation into consideration narrows the confidence intervals of the parameters. How to model the covariance structure in a bayesian longitudinal setting? I'm wondering if a bayesian intercept always introduces compound symmetry, similar to a random intercept in a frequentist linear mixed effects model? I suspect taking serial correlation would narrow the posterior distributions of the model parameters, strengthening the bayesian inference. However, I'm not at all sure if my thoughts are anywhere near correct. The brms package is a very valuable resource. However, the parts about covariance structures seem to be still in progress. If anyone has good theoretical (and why not practical) bayesian references regarding these covariance modeling issues (serial correlation etc.), I would appreciate them very much.
Anyone know why loo_predict(blm) and predict(blm[-obs.,],data[obs,]) are giving me a predicted odds of 0.28 and 0.8 respectively? These estimates are so far apart. “0.8” seems more accurate to me but the events true outcome was “0” so loo_predict did a better job. Does loo_predict just not work with high Pareto values, is that why??
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Thank you so much for building bmrs. You saved my master thesis, got me into Bayesian statistics and made me learn R, which is now a staple tool of my professional career.
Very valuable package indeed!
I'm wondering how to model the covariance structure in a bayesian longitudinal setting, similar to covariance patterns such as compound symmetry, autoregressive, Topelitz etc. in the frequentist world. In the frequentist world, taking serial correlation into consideration narrows the confidence intervals of the parameters.
How to model the covariance structure in a bayesian longitudinal setting? I'm wondering if a bayesian intercept always introduces compound symmetry, similar to a random intercept in a frequentist linear mixed effects model? I suspect taking serial correlation would narrow the posterior distributions of the model parameters, strengthening the bayesian inference. However, I'm not at all sure if my thoughts are anywhere near correct.
The brms package is a very valuable resource. However, the parts about covariance structures seem to be still in progress.
If anyone has good theoretical (and why not practical) bayesian references regarding these covariance modeling issues (serial correlation etc.), I would appreciate them very much.
(1:35) "A lot of us...a lot of us are."
The melancholy of that statement was so tangible :)
That was beautiful - Thank you for the wonderful package, Paul!
Is it possible to include a factor variable in the model? If yes any examples please.
How to consider priors in Bayesian regression with some data
Dont know
Anyone know why loo_predict(blm) and predict(blm[-obs.,],data[obs,]) are giving me a predicted odds of 0.28 and 0.8 respectively? These estimates are so far apart. “0.8” seems more accurate to me but the events true outcome was “0” so loo_predict did a better job. Does loo_predict just not work with high Pareto values, is that why??