A Non-Parametric Bayesian Method for Inferring Hidden Causes

Understand

We present a non-parametric Bayesian approach to structure learning with hidden causes.

• Previous Bayesian treatments of this problem define a prior over the number of hidden causes and use algorithms such as reversible jump Markov chain Monte Carlo to move between solutions.
• In contrast, we assume that the number of hidden causes is unbounded, but only a finite number influence observable variables.
• This makes it possible to use a Gibbs sampler to approximate the distribution over causal structures.