Bayesian Linear Regression With Pymc3 And A Large Dataset - Bracket Nesting Level Exceeded Maximum And Slow Performance

I would like to use a Bayesian multivariate linear regression to estimate the strength of players in team sports (e.g. ice hockey, basketball or soccer). For that purpose, I create

Solution 1:

Eliminating the for loops should improve performance and might also take care of the nesting issue you are reporting. Theano TensorVariables and the PyMC3 random variables that derive from them are already multidimensional and support linear algebra operations. Try changing your code to something along the lines of

beta = pm.Normal('beta', mu=0, sd=10, shape=X.shape[1])
...
mu = alpha + pm.math.dot(X, beta)
...

If you need specify different prior values for mu and/or sd, those arguments accept anything that theano.tensor.as_tensor_variable() accepts, so you can pass a list or numpy array.

I highly recommend getting familiar with the theano.tensor and pymc3.math operations since sometimes you must use these to properly manipulate random variables, and in general it should lead to more efficient code.