total_churn = [101] formula = 'θ ~ ' + ' + '.join(['%s' % variable for variable in x_train.columns]) with pm.Model() as model: family = pm.glm.families.Normal() fitted_θ = pm.glm.GLM.from_formula(formula, data = x_train, family = family) churn_trials = pm.Bernoulli('churn_trials',p=θ,sha…

Using glm as a prior to inference a hidden variable

Boyang_Zhang February 24, 2020, 3:43pm 4

I suppose let’s say I had enough evidence with more samples available, would this method be valid then?

Topic		Replies	Views
Sampling from glm posterior Questions	1	452	July 10, 2018
The pymc3 way: Linear regression and inferring given posterior/trace Questions	4	1780	May 9, 2018
Gaussian Process Regression level 1 inference: Re-producing Mauna Loa CO2 Example with PyMC3 Questions	10	2244	August 16, 2017
Hierarchical logistic regression Sharing	17	4456	October 28, 2019
Concepts of Parameter Estimation and Predictions, and Out of Sample Predicted Probability for Logistic Regression Questions	5	1464	May 11, 2018