parameter-learning

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erdogant / bnlearn

Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, 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 Oct 25, 2024
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nickcafferry / Machine-Learning-in-Molecular-Sciences

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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,…

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 May 16, 2022
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Parametric-Data-Assimilation / kse-multiparameter-learning

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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.

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