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https://github.com/idsia/crema
Crema: Credal Models Algorithms
https://github.com/idsia/crema
bayesian bayesian-models credal credal-models crema imprecise-probability inference probabilistic-graphical-models probability
Last synced: about 2 months ago
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Crema: Credal Models Algorithms
- Host: GitHub
- URL: https://github.com/idsia/crema
- Owner: IDSIA
- License: lgpl-3.0
- Created: 2020-05-14T15:58:04.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-03-06T10:19:11.000Z (10 months ago)
- Last Synced: 2024-03-21T17:10:49.849Z (10 months ago)
- Topics: bayesian, bayesian-models, credal, credal-models, crema, imprecise-probability, inference, probabilistic-graphical-models, probability
- Language: Java
- Homepage: https://crema-toolbox.readthedocs.io/
- Size: 451 MB
- Stars: 10
- Watchers: 4
- Forks: 4
- Open Issues: 15
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
- Citation: CITATION.bib
Awesome Lists containing this project
README
[](https://badge.fury.io/gh/idsia%2Fcrema)
CreMA is an open-source java toolbox that provides multiple
learning and inference algorithms for credal models.
An example of exact inference in a credal network is given below.
```java
import ch.idsia.crema.core.ObservationBuilder;
import ch.idsia.crema.core.Strides;
import ch.idsia.crema.factor.credal.vertex.separate.VertexFactor;
import ch.idsia.crema.factor.credal.vertex.separate.VertexFactorFactory;
import ch.idsia.crema.inference.ve.CredalVariableElimination;
import ch.idsia.crema.model.graphical.DAGModel;
import ch.idsia.crema.model.graphical.GraphicalModel;
public class Starting {
public static void main(String[] args) {
double p = 0.2;
double eps = 0.0001;
/* CN defined with vertex Factor */
// Define the model (with vertex factors)
GraphicalModel model = new DAGModel<>();
int A = model.addVariable(3);
int B = model.addVariable(2);
model.addParent(B, A);
// Define a credal set of the partent node
VertexFactor fu = VertexFactorFactory.factory().domain(model.getDomain(A), Strides.empty())
.addVertex(new double[]{0., 1 - p, p})
.addVertex(new double[]{1 - p, 0., p})
.get();
model.setFactor(A, fu);
// Define the credal set of the child
VertexFactor fx = VertexFactorFactory.factory().domain(model.getDomain(B), model.getDomain(A))
.addVertex(new double[]{1., 0.,}, 0)
.addVertex(new double[]{1., 0.,}, 1)
.addVertex(new double[]{0., 1.,}, 2)
.get();
model.setFactor(B, fx);
// Run exact inference
CredalVariableElimination inf = new CredalVariableElimination();
inf.query(model, ObservationBuilder.observe(B, 0), A);
}
}
```
## Installation
Add the following code in the pom.xml of your project:
```xml
cremaRepo
https://raw.github.com/idsia/crema/mvn-repo/
ch.idsia
crema
0.2.1
compile
```
## Citation
If you write a scientific paper describing research that made use of the CREMA library, please cite the following paper:
```
Huber, D., Cabañas, R., Antonucci, A., Zaffalon, M. (2020).
CREMA: a Java library for credal network inference.
In Jaeger, M., Nielsen, T.D. (Eds),
Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM 2020),
Proceedings of Machine Learning Research, PMLR, Aalborg, Denmark.
```
In BiBTeX format (for your convenience):
```bibtex
@INPROCEEDINGS{huber2020a,
title = {{CREMA}: a {J}ava library for credal network inference},
editor = {Jaeger, M. and Nielsen, T.D.},
publisher = {PMLR},
address = {Aalborg, Denmark},
series = {Proceedings of Machine Learning Research},
booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models ({PGM} 2020)},
author = {Huber, D. and Caba\~nas, R. and Antonucci, A. and Zaffalon, M.},
year = {2020},
url = {https://pgm2020.cs.aau.dk}
}
```