parameter-learning

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erdogant / bnlearn

Sponsor

Python package for learning the graphical structure of Bayesian networks, parameter learning, inference and sampling methods.

python machine-learning bayesian-inference dag causality-analysis structure-learning causal causal-models sampling-methods directed-acyclic-graph causal-networks parameter-learning

Updated Mar 19, 2022
Jupyter Notebook

nickcafferry / Machine-Learning-in-Molecular-Sciences

Star

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.

machine-learning random-forest clustering feature-selection dimensionality-reduction artificial-neural-networks gaussian-processes learning-curve kernel-ridge-regression delta-learning-rule parameter-learning

Updated Oct 9, 2020
Jupyter Notebook

rbalexan / aa-228

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workspace for AA 228: decision making under uncertainty

reinforcement-learning decision-making q-learning uncertainty mdp sarsa bayesian-inference bayesian-networks graph-search structure-learning pomdp probabilistic-models parameter-learning

Updated Jan 6, 2020
Julia

Parametric-Data-Assimilation / kse-multiparameter-learning

Star

An AOT-based algorithm to estimate multiple unknown parameters in the Kuramoto-Saviashinski equation. Source code for the paper "Concurrent Multi-parameter Learning Demonstrated on the Kuramoto-Sivashinsky Equation" by Pachev, Whitehead, and McQuarrie.

parameter-estimation data-assimilation nudging parameter-learning kuramoto-sivashinsky

Updated Jan 19, 2022
Python

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