1. A Non-Parametric Bayesian Method for Inferring Hidden Causes
by F. Wood, T. L. Griffiths and Z. Ghahramani
Discussion led by Qi An
ECE, Duke University

2. Outline Introduction A generative model with hidden causes
Inference algorithms
Experimental results
Conclusions

3. Introduction
A variety of methods from Bayesian statistics have been applied to find model structure from a set of observed variables
Find the dependencies among the set of observed variables
Introduce some hidden causes and infer their influence on observed variables

4. Introduction
Learning model structure containing hidden causes presents a significant challenge
The number of hidden causes is unknown and potentially unbounded
The relation between hidden causes and observed variables is unknown
Previous Bayesian approaches assume the number of hidden causes is finite and fixed.

5. A hidden causal structure
Assume we have T samples of N BINARY variables. Let be the data and be a dependency matrix among
Introduce K BINARY hidden causes with T samples. Let be hidden causes and be a dependency matrix between and
K can potentially be infinite.

6. A hidden causal structure
Hidden causes (Diseases)
Observed variables (Symptoms)

7. A generative model
Our goal is to estimate the dependency matrix Z and hidden causes Y.
From Bayes’ rule, we know
We start by assuming K is finite, and then consider the case where K∞

8. A generative model
Assume the entries of X are conditionally independent given Z and Y, and are generated from a noise-OR distribution.
where , ε is a baseline probability that , and λ is the probability with which any of hidden causes is effective

9. A generative model
The entries of Y are assumed to be drawn from a Bernoulli distribution
Each column of Z is assumed to be Bernoulli(θk) distributed. If we further assume a Beta(α/K,1) hyper-prior and integrate out θk
where
These assumptions on Z are exactly the same as the assumption in IBP

10. Taking the infinite limit
If we let K approach infinite, the distribution on X remains well-defined, and we only need to concern about rows in Y that the corresponding mk>0.
After some math and reordering of Z, the distribution on Z can be obtained as

11. The Indian buffet process is defined in terms of a sequence of N customers entering a restaurant and choosing from an infinite array of dishes. The first customer tries the first Poisson(α) dishes. The remaining customers then enter one by one and pick previously sampled dishes with probability and then tries Poisson(α/i) new dishes.

12. Reversible jump MCMC

13. Gibbs sampler for Infinite case

14. Experimental results
Synthetic Data

15. number of iterations

16. Real data

17. Conclusions
A non-parametric Bayesian technique is developed and demonstrated
Recovers the number of hidden causes correctly and can be used to obtain reasonably good estimate of the causal structure
Can be integrated into Bayesian structure learning both on observed variables and on hidden causes.