Hi everyone, I have a question that I can’t seem to easily answer.
Once I get the full posterior distribution of a parameter, let’s say \beta_1, during inference.
How do I get the density of the parameter being over 1, 2, 3 or some value ?
How do I easily tell and show that 20% of the prob. density is under 0.5 or over 1.3 for example?
Do you have the trace with the posterior samples? If so you can use numpy for simple stats
As Nicholas said, it’s the beauty of Bayesian inference: you just have to count the scenarios of interest among all the possible scenarios in your model.
In addition to stats, you can use
ref_val argument: https://arviz-devs.github.io/arviz/generated/arviz.plot_posterior.html#arviz.plot_posterior
Exactly that, thank you!!
Good stuff, thank you guys for the help =)