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author | Cathy Yeh <cathy@driver.xyz> | 2018-02-03 19:50:34 -0800 |
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committer | Cathy Yeh <cathy@driver.xyz> | 2018-02-06 10:01:16 -0800 |
commit | c3f5f71c4cd3eef45ac430e64b80085a1205a624 (patch) | |
tree | 460e2d3de9e9cc9e3f115d12dfc3840d7b036ca1 /README.md | |
parent | c99643c7485bbdadf52605cb9243d957fd0489bc (diff) | |
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readme
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@@ -1 +1,86 @@ # beliefs + +A library for performing inference with Bayesian Networks for a +special use case, derived +from [pgmpy](https://github.com/pgmpy/pgmpy). + + +## Motivation + +**Exact inference** + +This library provides the ability to perform exact inference in a +computationally tractable* way for a specific but useful case: Bayesian +Networks with +* polytree structure +* consisting of Bernoulli random variables whose relationship to their + parents in the probabilistic graphical model are described by AND or + OR logic + +Non-deterministic conditional probability distributions for +multinomial, discrete random variables are also supported, although +the algorithm is specifically optimized for the case of Bernoulli AND +and Bernoulli OR variables. + +*See the "Many parents model" in + the + [jupyter notebook](https://render.githubusercontent.com/view/ipynb?commit=73aa4a35d08f1c16569bc78d176710381b9e9605&enc_url=68747470733a2f2f7261772e67697468756275736572636f6e74656e742e636f6d2f64726976657267726f75702f62656c696566732f373361613461333564303866316331363536396263373864313736373130333831623965393630352f6578616d706c65732f636f6d706172655f70676d70795f62656c6965665f70726f7061676174696f6e2e6970796e623f746f6b656e3d4158386c536f5a35622d2d7848394a736a58727a65345a7846587531333150426b733561646f4f567741253344253344&nwo=drivergroup%2Fbeliefs&path=examples%2Fcompare_pgmpy_belief_propagation.ipynb&repository_id=110306600&repository_type=Repository#IV.-Many-parents-model) under + the examples/ directory for an example of a case in which + inference becomes computationally intractable with pgmpy but can + be handled by beliefs optimized algorithm. + +## Additional features + +* In addition to being able to perform inference based on direct + observation of a variable in the PGM, beliefs also provides the + ability to specify virtual evidence, i.e. evidence that modifies the + belief, or marginal probability, of a variable by affecting its + likelihood based on observations of variables not in the PGM, while + not pinning the variable into a definite (observed) state. +* The ability to catch conflicting evidence errors during inference, + which manifest as numpy NaNs in pgmpy's inference results. +* Utility for gathering the direct observations that influenced the + beliefs of variables that were inferred to be in a definite state. + + +## Getting started + +### Installation + +Using conda: +``` +conda install -c driver beliefs +``` + +### Example + +Perform inference on a Bayesian Network: +```python +from beliefs.inference.belief_propagation import BeliefPropagation +from beliefs.models.belief_update_node_model import ( + BeliefUpdateNodeModel, + BernoulliOrNode +) + +# directed edges for a polytree Bayes Net +edges = [('u', 'x'), ('v', 'x'), ('x', 'y'), ('x', 'z') + +# initialize model w/ edges, default to OR CPD for all variables +model = BeliefUpdateNodeModel.init_from_edges(edges, BernoulliOrNode) + +# initialize inference +infer = BeliefPropagation(model) + +# perform inference, with 'x' is observed to be True. +result = infer.query(evidence={'x': np.array([0, 1])}) +``` + +## Tests + +From the project root directory: +``` +pytest tests -vv +``` + +## License +This project is licensed under the terms of the MIT license. |