Hands-On Machine Learning for Algorithmic Trading

How to select priors

The prior should reflect knowledge of the distribution of the parameters because it influences the MAP estimate. If a prior is not known with certainty, we need to make a choice, often from several reasonable options. In general, it is good practice to justify the prior and check for robustness by testing whether alternatives lead to the same conclusion.

There are several types of priors:

• Objective priors maximize the impact of the data on the posterior. If the parameter distribution is unknown, we can select an uninformative prior like a uniform distribution, also called a flat prior, over a relevant range of parameter values.
• In contrast, subjective priors aim to incorporate information that's external to the model ...