auroranil / bayes.js

Javascript library that simulates a bayesian network. Infrencing, sampling and d-seperation.

Bayes.js

A javascript bayesian network simulation library. A demo can be found here.

Installing

Just insert bayes.js or bayes.min.js into your project, and you are good to go.

##Using

Creating a network:

var network = new BayesNet ({`
"r": {children : ["s", "w"], parents : [],    observation : `"none", blocks : false , CPT : [[0.2]] },    
"s": {children : ["w"], parents : ["r"], observation : "none", blocks : false, CPT :[[ 0.01 ], [0.4]]},
"w": {children : [], parents : ["r", "s"], observation : "none", blocks : false, CPT : [[0.99], [ 0.8 ], [ 0.9 ], [ 0.0 ]]}
});

Set evidence: network.varible["r"].observation = "T";

Approximate inference: network.rejectionSample("s",100);

Building from source

uglify-js is required to build a minified version. Otherwise, just clone the repo and run make.

Acknowledgements:

Underscore.js is used to build this library. This was inspired by my various AI classes.

I would like this library to be as useful as possible, so please don't hesitate to send me feedback or a pull request if there are problems.