Determing the probability distribution of coefficients given a particular value

[Helios1014]

Say I have a set of coefficients b ={b1,b2,…bn} with their own distributions and I decided to set the value of b1 to a particular value x. How could I determine, if this question even makes sense, what the probability distributions for b are conditional on b1 being equal to x?

Is there someone who has already explored this question in a notebook that is readily available?

My end goal is a visual where the viewer can take the model, set various beta values and see how the distributions my be effected—if they are at all.

[jessegrabowski]

This discussion might be relevant?

Samples from Conditional Posterior Distribution

Let us consider the following Hierarchical Bayesian model: w \sim\ Beta(20, 20) K = 6 a = w * (K - 2) + 1 b = (1 - w) * (K - 2) + 1 theta \sim\ Beta(a, b) y \sim\ Bern(theta) The above is the example of figure 9.3 in the book Doing Bayesian Data Analysis by John Kruschke. I use the pymc3 to construct the model as follows: import numpy as np import pymc3 as pm import arviz as az K = 6 D = np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]) with pm.Model() as mdl: w = pm.Beta('w', alpha…

[Helios1014]

Thank you