Alternatives and similar repositories for aMC:

Users that are interested in aMC are comparing it to the libraries listed below

• sydney-machine-learning / Bayesianneuralnetworks-MCMC-tutorial
  Bayesian neural networks via MCMC: tutorial
  ☆51Updated 5 months ago
• iaifi / summer-school-2024
  ☆22Updated 7 months ago
• ziatdinovmax / NeuroBayes
  Fully and Partially Bayesian Neural Nets
  ☆66Updated 3 weeks ago
• BallisticLA / parla
  Python Algorithms for Randomized Linear Algebra
  ☆53Updated last year
• alu042 / SDE-higham
  Some basic algorithms for stochastic differential equations in Python/NumPy
  ☆28Updated 11 years ago
• DrSasanBarak / AugmentTS
  AugmentTS :: Time Series Data Augmentation using Deep Generative Models
  ☆57Updated 3 years ago
• datasig-ac-uk / RoughPy
  Toolbox for working with streaming data as rough paths in Python
  ☆36Updated this week
• LRydin / KramersMoyal
  kramersmoyal: Kramers-Moyal coefficients for stochastic data of any dimension, to any desired order
  ☆72Updated 3 months ago
• tiskw / random-fourier-features
  Implementation of random Fourier features for kernel method, like support vector machine and Gaussian process model
  ☆100Updated 5 months ago
• heal-research / pyoperon
  Python bindings and scikit-learn interface for the Operon library for symbolic regression.
  ☆49Updated 2 weeks ago
• NBoulle / RationalNets
  Code for the paper "Rational neural networks", NeurIPS 2020
  ☆28Updated 4 years ago
• nplresearch / higher_order_LRG
  Python code to perform higher-order Laplacian renormalization of higher-order networks
  ☆21Updated 6 months ago
• E-Renshaw / kuramoto-sivashinsky
  A simulation of the Kuramoto-Sivashinsky Equation in Python and MATLAB
  ☆25Updated 6 years ago
• lanl / SEPIA
  Simulation-Enabled Prediction, Inference, and Analysis: physics-informed statistical learning.
  ☆35Updated last year
• josephbakarji / bucki-data
  Machine learning algorithms for discovering dimensionless groups from simulation and experimental data
  ☆12Updated 2 years ago
• PhIMaL / temporal_normalizing_flows
  ☆11Updated 4 years ago
• dynamicslab / langevin-regression
  Code for "Nonlinear stochastic modeling with Langevin regression" J. L. Callaham, J.-C. Loiseau, G. Rigas, and S. L. Brunton
  ☆25Updated 3 years ago
• wongswk / magi
  ☆13Updated 5 months ago
• crispitagorico / sigkernel
  Differentiable computations for the signature-PDE-kernel on CPU and GPU.
  ☆53Updated 10 months ago
• Alireza-Akhavan / graph-neural-network
  Tutorial on deep learning models that can work with graph data
  ☆27Updated last year
• gngdb / pytorch-minimize
  Use scipy.optimize.minimize as a PyTorch Optimizer.
  ☆72Updated 8 months ago
• SamirMoustafa / Time-Series-Classification
  Topological Features for Time Series Classification
  ☆16Updated 4 years ago
• long-da / A-United-Framework-to-Integrate-Physics-into-Gaussian-Processes
  ☆10Updated 2 years ago
• ds4m / topological-data-analysis
  Code for the Topological Data Analysis chapter of Data Science for Mathematicians
  ☆20Updated 5 years ago
• cornellius-gp / linear_operator
  A LinearOperator implementation to wrap the numerical nuts and bolts of GPyTorch
  ☆107Updated 3 weeks ago
• PredictiveScienceLab / advanced-scientific-machine-learning
  ME 697 - Advanced Scientific Machine Learning
  ☆21Updated last week
• MilesCranmer / pysr_paper
  A paper describing the implementation of PySR and SymbolicRegression.jl
  ☆54Updated last year
• austindowney / Physics-Informed-Machine-Learning-Example
  A basic example of using physics informed machine learning for enhanced structural dynamics modeling
  ☆9Updated last year
• barnesd8 / machine_learning_for_persistence
  ☆11Updated last year
• yongkyung-oh / Stable-Neural-SDEs
  [ICLR2024] Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data
  ☆33Updated 2 months ago