xiaoxuchen

xiaoxuchen's Stars

• xiaoxuchen/Markov-Regime-switching-Model

  Language:Jupyter Notebook21

• werleycordeiro/Dynamic_Nelson_Siegel_Svensson_Kalman_Filter

  Python Package: Fitting and Forecasting the yield curve

  Language:Python326

• AndyandViky/HDP-GMM

  Variational Inference Hierarchical Dirichlet process Mixture Models of Gaussian Distribution

  Language:Python41

• xinychen/latex-cookbook

  LaTeX论文写作教程 (清华大学出版社)

  Language:Jupyter Notebook1.3k147

• fhlyhv/VWAMultiGGM

  Variational Wishart Approximation for Multiscale/Multiresolution Graphical Models

  Language:MATLAB12

• sremes/wishart-gibbs-kernel

  A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings (ACML 2017)

  Language:Matlab42

• ckheaukulani/swpr

  Variational inference for Wishart and inverse Wishart processes

  Language:Python83

• xinychen/awesome-latex-drawing

  Drawing Bayesian networks, graphical models, tensors, technical frameworks, and illustrations in LaTeX.

  Language:TeX1.3k153

• markdregan/Bayesian-Modelling-in-Python

  A python tutorial on bayesian modeling techniques (PyMC3)

  Language:Jupyter Notebook2.5k412

• wlwkgus/GibbsNet

  Implementation of paper "GibbsNet: Iterative Adversarial Inference for Deep Graphical Models" in PyTorch

  Language:Python586

• krasserm/bayesian-machine-learning

  Notebooks about Bayesian methods for machine learning

  Language:Jupyter Notebook1.8k459

• skggm/skggm

  Scikit-learn compatible estimation of general graphical models

  Language:Python23744

• daft-dev/daft

  Render probabilistic graphical models using matplotlib

  Language:Python675118

• py-why/dowhy

  DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

  Language:Python7k925

• pgmpy/pgmpy_notebook

  Short Tutorial to Probabilistic Graphical Models(PGM) and pgmpy

  Language:Jupyter Notebook369212

• kushagra06/CS228_PGM

  🌀 Stanford CS 228 - Probabilistic Graphical Models

  Language:Python11256