Try pyINLA Online
Run pyINLA directly in your browser. No installation required - just click and start modeling.
What you'll learn:
from pyinla import pyinla
# Define your model
model = {
'response': 'price',
'fixed': ['1', 'size']
}
# Fit and get Bayesian posteriors in seconds
result = pyinla(model, family='gaussian', data=df)
print(result.summary_fixed)
What's in the demo notebook
Linear regression with posterior visualization
Poisson regression for count data
Mixed effects / random effects models
Upload and test your own data