xiaoxuchen's Stars
xiaoxuchen/Markov-Regime-switching-Model
werleycordeiro/Dynamic_Nelson_Siegel_Svensson_Kalman_Filter
Python Package: Fitting and Forecasting the yield curve
AndyandViky/HDP-GMM
Variational Inference Hierarchical Dirichlet process Mixture Models of Gaussian Distribution
xinychen/latex-cookbook
LaTeX论文写作教程 (清华大学出版社)
fhlyhv/VWAMultiGGM
Variational Wishart Approximation for Multiscale/Multiresolution Graphical Models
sremes/wishart-gibbs-kernel
A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings (ACML 2017)
ckheaukulani/swpr
Variational inference for Wishart and inverse Wishart processes
xinychen/awesome-latex-drawing
Drawing Bayesian networks, graphical models, tensors, technical frameworks, and illustrations in LaTeX.
markdregan/Bayesian-Modelling-in-Python
A python tutorial on bayesian modeling techniques (PyMC3)
wlwkgus/GibbsNet
Implementation of paper "GibbsNet: Iterative Adversarial Inference for Deep Graphical Models" in PyTorch
krasserm/bayesian-machine-learning
Notebooks about Bayesian methods for machine learning
skggm/skggm
Scikit-learn compatible estimation of general graphical models
daft-dev/daft
Render probabilistic graphical models using matplotlib
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.
pgmpy/pgmpy_notebook
Short Tutorial to Probabilistic Graphical Models(PGM) and pgmpy
kushagra06/CS228_PGM
🌀 Stanford CS 228 - Probabilistic Graphical Models