<commit_before>## SAMCNet

SAMCNet is a toolkit and demonstration for Bayesian model averaging over 
objective functions defined over model classes of interest.

Specifically, we are here using the model class of discrete, static Bayesian 
networks defined over the variables of interest, and using objective functions 
to simplify the complex posterior over this large model class.

I'll go more into detail regarding the theory, code, and applications once I 
have some papers published.

## Building Blocks

This software would not be possible without the following components:
- Python for the main driving and glue code
- Cython for C and C++ integration and speed
- [libdai](http://cs.ru.nl/~jorism/libDAI/) for Bayesian network inference.
- [Redis](http://redis.io) for the distributed job management
- [waf](http://code.google.com/p/waf/) for the build system
- rsyslog for remote logging<commit_msg>Made todo list in README<commit_after>## SAMCNet

SAMCNet is a toolkit and demonstration for Bayesian model averaging over 
objective functions defined over model classes of interest.

Specifically, we are here using the model class of discrete, static Bayesian 
networks defined over the variables of interest, and using objective functions 
to simplify the complex posterior over this large model class.

I'll go more into detail regarding the theory, code, and applications once I 
have some papers published.

## Building Blocks

This software would not be possible without the following components:
- Python for the main driving and glue code
- Cython for C and C++ integration and speed
- [libdai](http://cs.ru.nl/~jorism/libDAI/) for Bayesian network inference.
- [Redis](http://redis.io) for the distributed job management
- [waf](http://code.google.com/p/waf/) for the build system
- rsyslog for remote logging

## TODO

1. Estimate class conditional density by
    1. Discretizing space
    2. Weighted SAMC average sample densities at each point
2. Find decision boundary. I think it's enough to:
    1. Wrap these effective densities in an interpolation function
    2. Numerically find the intersection between the two classes effective 
       densities for a predetermined set of x (or y) values.
3. Then calculate the analytical boundary using Lori's methods
    1. Plot the two on top of each other
    2. Quantify the difference? Could do this using true error using the known 
       ground truth densities.
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