suwoncjh / awesome-latex-drawing

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

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Awesome LaTeX drawing

This project covers a lot of LaTeX codes for drawing Bayesian networks, graphical models, and technical framework.

Contents

Usage

For many programming languages like Python, installing related packages is just the first step. Fortunately, you do not even to install any packages or even LaTeX in your PC (personal computer) because there are many online systems like overleaf make it easy to use.

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.

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 our overleaf project, then, you will see the following picture:

drawing

Awesome Stuff

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

drawing

trandim logo.

Related Projects

Our Publications

Most of these examples are from our publications:

  • 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.

About

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

License:MIT License


Languages

Language:TeX 100.0%