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. densi…

How to get the probability of being over/under a certain value for a parameter?

nkaimcaudle March 24, 2020, 11:52pm 2

Do you have the trace with the posterior samples? If so you can use numpy for simple stats

np.mean(trace['beta_1']>1.3)

4 Likes

Topic		Replies	Views
Simple way to obtain posterior probability of slope > Questions	1	358	August 20, 2021
Estimating probability of data point using inferred posterior Questions	7	775	April 30, 2018
How to condition on a parameter Questions	2	998	March 3, 2018
Compute distribution of percentile of score Questions	2	659	April 22, 2018
A question about the scope of the variable Questions	4	481	August 8, 2018