GitHub topics: parameter-learning
erdogant/bnlearn
Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.
Language: Jupyter Notebook - Size: 41.6 MB - Last synced at: 18 days ago - Pushed at: 19 days ago - Stars: 514 - Forks: 50
Parametric-Data-Assimilation/kse-multiparameter-learning
An AOT-based algorithm to estimate multiple unknown parameters in the Kuramoto-Saviashinski equation. Source code for the paper "Concurrent Multiparameter Learning Demonstrated on the Kuramoto-Sivashinsky Equation" by Pachev, Whitehead, and McQuarrie.
Language: Python - Size: 30.3 KB - Last synced at: over 1 year ago - Pushed at: over 2 years ago - Stars: 2 - Forks: 0
nickcafferry/Machine-Learning-in-Molecular-Sciences
2017 Summer School on the Machine Learning in the Molecular Sciences. This project aims to help you understand some basic machine learning models including neural network optimization plan, random forest, parameter learning, incremental learning paradigm, clustering and decision tree, etc. based on kernel regression and dimensionality reduction, feature selection and clustering technology.
Language: Jupyter Notebook - Size: 109 MB - Last synced at: over 2 years ago - Pushed at: about 3 years ago - Stars: 5 - Forks: 1
Venn1998/Diabetes-BayesianModel
Bayesian Network that encodes the relationships between diabetes, its risk factors, and its effects
Language: Jupyter Notebook - Size: 765 KB - Last synced at: over 2 years ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0
rbalexander1/aa-228
workspace for AA 228: decision making under uncertainty
Language: Julia - Size: 51.3 MB - Last synced at: 3 days ago - Pushed at: over 5 years ago - Stars: 2 - Forks: 1
