Stars
Python Package: Fitting and Forecasting the yield curve
Variational Inference Hierarchical Dirichlet process Mixture Models of Gaussian Distribution
Variational Wishart Approximation for Multiscale/Multiresolution Graphical Models
A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings (ACML 2017)
Variational inference for Wishart and inverse Wishart processes
Drawing Bayesian networks, graphical models, tensors, technical frameworks, and illustrations in LaTeX.
A python tutorial on bayesian modeling techniques (PyMC3)
Implementation of paper "GibbsNet: Iterative Adversarial Inference for Deep Graphical Models" in PyTorch
Notebooks about Bayesian methods for machine learning
Scikit-learn compatible estimation of general graphical models
Render probabilistic graphical models using matplotlib
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 graphic…
Short Tutorial to Probabilistic Graphical Models(PGM) and pgmpy
🌀 Stanford CS 228 - Probabilistic Graphical Models