zhangcun Yan's Projects
Active Bayesian Causal Inference (Neurips'22)
Artificial Intelligence projects, documentation and code.
Hierarchical forecasting
A selection of state-of-the-art research materials on trajectory prediction
Python Code for the Lecture "Verhaltensgenerierung für Fahrzeuge" (Behavior Generation for Vehicles) at KIT
Bayes Net Toolbox for Matlab
Causal Inference with the package "bnlearn"
Causal Inference and Discovery in Python by Packt Publishing
Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.
Julia code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins
Python code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins
A Python library that helps data scientists to infer causation rather than observing correlation.
《动手学深度学习》:面向中文读者、能运行、可讨论。中英文版被70多个国家的500多所大学用于教学。
:exclamation: This is a read-only mirror of the CRAN R package repository. dbnR — Dynamic Bayesian Network Learning and Inference. Homepage: https://github.com/dkesada/dbnR
Intro to Double ML
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.
DynGFN: Bayesian Dynamic Causal Discovery using Generative Flow Networks
Code for the implementation of various methods of Non-Homogeneous Dynamic Bayesian Networks inference
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal in
2021年最新总结,推荐工程师合适读本,计算机科学,软件技术,创业,**类,数学类,人物传记书籍
The only guide you need to learn everything about GMM
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Interaction Dataset Python Scripts
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
Open Content for self-directed learning in data science
This repository is used as a support for the paper "" (to be named)