/awesome-latex-drawing

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

Primary LanguageTeXMIT LicenseMIT

Awesome LaTeX drawing

MIT License repo size GitHub stars

Made by Xinyu Chen • 🌐 https://twitter.com/chenxy346

awesome-latex-drawing is a collection which uses LaTeX to draw Bayesian networks, graphical models, and technical frameworks.

LaTeX is a high-quality typesetting system, and it is available as free software. In recent years, it is popular for creating some graphics by LaTeX because LaTeX is able to draw many complicated graphics containing equations. For many programming languages like Python, installing related packages is just the first step. If you prefer not to install LaTeX on your own computer, overleaf.com is a good option. overleaf.com runs in your web browser and be accessed from any computer with an Internet connection. It is really convenient! Please register an account first!

Contents

Usage

Open overleaf.com in your Chrome.

It is not necessary to open each file in this repository because you can just follow this readme document.

Our Examples

Bayesian Networks

  • Open BCPF.tex in your overleaf project, then you will see the following picture:

drawing

BCPF (Bayesian CP factorization) model as a Bayesian network and a directed factor graph.

  • Open BGCP.tex in your overleaf project, then you will see the following pictures:

drawing

BGCP (Bayesian Gaussian CP decomposition) model as a Bayesian network and a directed factor graph.

  • Open BGCP-1.tex in your overleaf project, then you will see the following picture:

drawing

Another example for BGCP (Bayesian Gaussian CP decomposition) model as a Bayesian network and a directed factor graph.

  • Open BATF.tex in your overleaf project, then you will see the following picture:

drawing

BATF (Bayesian augmented tensor factorization) model as a Bayesian network and a directed factor graph.

  • Open btmf.tex in your overleaf project, then you will see the following picture:

drawing

BTMF (Bayesian temporal matrix factorization) model as a Bayesian network and a directed factor graph.

  • Open BTMF.tex in your overleaf project, then you will see the following picture:

drawing

BTMF (Bayesian temporal matrix factorization) model as a Bayesian network and a directed factor graph.

Research Frameworks

in your overleaf project, then you will see the following picture:

drawing

Tensor completion task and its framework including data organization and tensor completion, in which traffic measurements are partially observed.

drawing

A graphical illustration of rolling prediction strategy with temporal matrix factorization and autoregressive model.

drawing

A graphical illustration of rolling prediction strategy with temporal matrix factorization and vector autoregressive model.

drawing

A graphical illustration of the partially observed time series data.

drawing

A graphical illustration of the partially observed time series tensor.

drawing

Multivariate time series data prediction with missing values.

drawing

Tensor time series data prediction with missing values.

  • Open mf-explained.tex in your overleaf project, then you will see the following picture:

drawing

A graphical illustration of matrix factorization.

in your overleaf project, then you will see the following picture:

drawing

  • Open latc_framework in your overleaf project, then you will see the following picture:

drawing

Tensor Factorization

  • Open tensor.tex in your overleaf project, then you will see the following picture:

drawing

A graphical illustration for the (origin,destination,time slot) tensor.

  • Open AuTF.tex in your overleaf project, then you will see the following picture:

drawing

Augmented tensor factorization (AuTF) model in our recent study.

  • Open TVART.tex in your overleaf project, then you will see the following picture:

drawing

  • Open tensor_svt.tex in your overleaf project, then you will see the following picture:

drawing

Data Visualization

  • Open RMseries.tex in your overleaf project, then you will see the following picture:

drawing

  • Open NMseries.tex in your overleaf project, then you will see the following picture:

drawing

in your overleaf project, then you will see the following picture:

drawing

If you want to draw each sub-figure, please check out the following .tex files:

Awesome Stuff

in your overleaf project, then, you will see the following picture:

drawing

trandim logo.

More Features

Coming soon...

  • Documentation (English version)
  • 技术文档 (中文版)

Related Projects

Publications

Most of these examples are from our papers:

  • Xinyu Chen, Yixian Chen, Lijun Sun (2020). Scalable low-rank autoregressive tensor learning for spatiotemporal traffic data imputation. arXiv: 2008.03194. [preprint] [data] [Python code]

  • Xinyu Chen, Jinming Yang, Lijun Sun (2020). A nonconvex low-rank tensor completion model for spatiotemporal traffic data imputation. arxiv. 2003.10271. [preprint] [data & Python code]

  • Xinyu Chen, Lijun Sun (2019). Bayesian temporal factorization for multidimensional time series prediction. arxiv. 1910.06366. [preprint] [slide] [data & Python code]

  • Xinyu Chen, Zhaocheng He, Yixian Chen, Yuhuan Lu, Jiawei Wang (2019). Missing traffic data imputation and pattern discovery with a Bayesian augmented tensor factorization model. Transportation Research Part C: Emerging Technologies, 104: 66-77. [preprint] [doi] [slide] [data] [Matlab code]

  • Xinyu Chen, Zhaocheng He, Lijun Sun (2019). A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation. Transportation Research Part C: Emerging Technologies, 98: 73-84. [preprint] [doi] [data] [Matlab code] [Python code]

    Please consider citing our papers if you find these codes help your research.