BayesianInference4GW_GWData_Bootcamp

The complete materials for the Gravitational Wave Data Exploration: A Practical Training in Programming and Analysis can be found in this GitHub repo.

Sci Talk: Bayesian inference for gravitational-wave science (Guest Lecture by Junjie Zhao)

Description

  - Brief introduction to gravitational wave (引力波简要介绍)
  - Part I: Bayesian inference (贝叶斯推断)
      - Definition of “probability” ("概率"的定义)
      - Rethink the interpretations (重思概率诠释)
          - Frequentist statistics (频率学派)
          - Bayesian statistics (贝叶斯学派)
      - Bayes' theorem (贝叶斯定理)
          - Application to the detection of gravitational wave (在引力波探测上应用)
      - Bayesian inference framework (贝叶斯推断框架)
          - Parameter estimation for gravitational-wave data (引力波数据分析中参数估计)
          - Model selection for gravitational-wave data (引力波数据分析中模型选择)
  - Q & A
  - Part II: Bayesian computation (贝叶斯计算方法)
      - Markov Chain Monte Carlo (MCMC; 马尔可夫链-蒙特卡罗方法)
          - hands-on tiny mcmc example
      - Nested sampling (嵌套采样)
          - hands-on tiny nested-sampling example
  - Part III: All in gravitational-wave data (一切尽在引力波数据中)
      - Use Bilby & Parallel Bilby in the GW data analysis
      - nShow the complete pipeline for the data analysis
  - The AMAZING Thomas Bayes (为美好的世界献上"贝叶斯定理")
  - Q & A

Date：2023/12/16 | Video recording | Slide: PDF
Date：2023/12/17 | Video recording | Slide: PDF

Talk: Bayesian inference for gravitational-wave science

About the authors

Seminar Coordinator: He Wang ( hewang@ucas.ac.cn )
Seminar Designer: Junjie Zhao (junjiezhao@bnu.edu.cn)

Dr. Junjie Zhao (赵俊杰) received his Ph.D. degree in theoretical physics from Peking University in 2021 and is currently doing scientific research as a "LiYun" postdoctoral fellow (励耘博士后) in the Department of Astronomy, Beijing Normal University. The main research interests are gravitational-wave physics, testing gravity, physics of pulsar, etc.

Table of Contents (内容概览)

Below is the overview of this seminar.

Brief introduction to gravitational wave (引力波简要介绍)

Part I: Bayesian inference (贝叶斯推断)
- Definition of “probability” ("概率"的定义)
- Rethink the interpretations (重思概率诠释)
  - Frequentist statistics (频率学派)
  - Bayesian statistics (贝叶斯学派)
- Bayes' theorem (贝叶斯定理)
  - Application to the detection of gravitational wave (在引力波探测上应用)
- Bayesian inference framework (贝叶斯推断框架)
  - Parameter estimation for gravitational-wave data (引力波数据分析中参数估计)
  - Model selection for gravitational-wave data (引力波数据分析中模型选择)
Q & A
Part II: Bayesian computation (贝叶斯计算方法)
- Markov Chain Monte Carlo (MCMC; 马尔可夫链-蒙特卡罗方法)
  - hands-on tiny mcmc example
- Nested sampling (嵌套采样)
  - hands-on tiny nested-sampling example
Part III: All in gravitational-wave data (一切尽在引力波数据中)
- Use Bilby & Parallel Bilby in the GW data analysis
- Show the complete pipeline for the data analysis
The AMAZING Thomas Bayes (为美好的世界献上"贝叶斯定理")
Q & A

The environment for the GW analysis

Here, I recommend using the conda or mamba command to manage your environment. Please make sure you have installed the Miniforge / Miniconda / Anaconda software.

Recommend: you can always replace the conda with the mamba (alternative faster conda) to manage your environment.

You can run

conda --version
mamba --version

to check your conda or mamba environment.

Warning: if you are using the Windows computer, please install WSL2 or use the remote Linux server to obtain the best experience.

Create the `full` environment [Recommend for the professional user]

To create the full environment, run the following command:

bash ./envs/update_igwn_envs.sh

If your network is blocked, please try

conda env update -f ./envs/igwn-py310-linux-64.yaml

for the Linux x86-64 and Linux amd64 architecture.

For the macOS x86-64 architecture

conda env update -f ./envs/igwn-py310-osx-64.yaml

For the macOS arm64 (Apple silicon)

conda env update -f ./envs/igwn-py310-osx-arm64.yaml

Create the `tiny` environment

conda create -n igwn-py310 python=3.10 numpy scipy lalsuite pycbc bilby parallel-bilby dynesty emcee jupyterlab ipympl ipywidgets

More advanced commands for conda / mamba can be found at Managing environments

hermione-evans/BayesianInference4GW_GWData_Bootcamp