Hands-On Machine Learning for Algorithmic Trading

Approximate inference – variational Bayes

The interface for variational inference is very similar to the MCMC implementation. We just use the fit() function instead of the sample() function, with the option to include an early stopping CheckParametersConvergence callback if the distribution-fitting process converged up to a given tolerance:

with logistic_model:    callback = CheckParametersConvergence(diff='absolute')    approx = pm.fit(n=100000,                     callbacks=[callback])

We can draw samples from the approximated distribution to obtain a trace object like we did previously for the MCMC sampler:

trace_advi = approx.sample(10000)

Inspection of the trace summary shows that the results are slightly less accurate.