Introduction to Statistics for Astronomers and Physicists

The lecture notes are directly accessible here in two formats:

1. The HTML-page format presents the lectures each on a single HTML page, with a persistent table of contents and dynamic media. LaTeX, images, and code are all rendered in-place. For those who prefer, the page can be switched between light and dark mode using the toggle button beneath the table of contents.
2. The HTML-slides format presents the lectures on a series of HTML slides. The slides are somewhat unconventional, in that they are variable in length (i.e. not like powerpoint). However I have found this to be the best format for conveying the information here.

Additional formats (PDF, markdown, and rmarkdown) are available in the git repository. These formats have limitations, however, as they variously cannot display animations (PDF) or latex (markdown) in a generally satisfactory way. The lectures themselves are written in rmarkdown, and can be compiled into the other formats within R/Rstudio.

HTML Page Notes

• Lecture 0: Course Outline and a Crash Course in R and Python
• Lecture 1a: Data Description and Summarisation
• Lecture 1b: Data Description, Analysis, and Modelling
• Lecture 1c: Data Mining Exercise
• Lecture 2a: Fundamentals of Probability I
• Lecture 2b: Fundamentals of Probability II
• Lecture 2c: Probability Distributions
• Lecture 2d: Random Numbers, Simulation, and Sampling
• Lecture 3a: Bayesian Statistics
• Lecture 3b: Priors and Introduction to Posterior Analysis
• Lecture 3c: Posterior Analysis II
• Lecture 4a: Significance of Evidence
• Lecture 4b: Optimisation and Complex Modelling I

• Lecture 4c: Complex Modelling II and Machine Learning

• Astrostatistics: Lecture 1

• Astrostatistics: Lecture 2
• Astrostatistics: MH Example
• Astrostatistics: Lecture 3

PDF Handouts

• Astrostatistics: Lecture 1
• Astrostatistics: Lecture 2
• Astrostatistics: MH Example

• Astrostatistics: Lecture 3

• Ringvorlesung: Astrostatistics

HTML Short-Format Slides

• Lecture 0: Course Outline and a Crash Course in R and Python

HTML Long-Format Slides

• Lecture 0: Course Outline and a Crash Course in R and Python
• Lecture 1a: Data Description and Summarisation
• Lecture 1b: Data Description, Analysis, and Modelling
• Lecture 1c: Data Mining Exercise
• Lecture 2a: Fundamentals of Probability I
• Lecture 2b: Fundamentals of Probability II
• Lecture 2c: Probability Distributions
• Lecture 2d: Random Numbers, Simulation, and Sampling
• Lecture 3a: Bayesian Statistics
• Lecture 3b: Priors and Introduction to Posterior Analysis
• Lecture 3c: Posterior Analysis II
• Lecture 4a: Significance of Evidence
• Lecture 4b: Optimisation and Complex Modelling I
• Lecture 4c: Complex Modelling II and Machine Learning
• Ringvorlesung: Astrostatistics

Cloning the lecture notes

The lecture notes can be directly downloaded in many formats from the github repository, or from the command line as below:

git clone git@github.com:AngusWright/AstroStats.git

Introduction to Statistics for Astronomers and Physicists

Lecture Recordings

Recordings of the Lectures from the Summer Semester 2021 (held virtually) are available on my YouTube channel in the Lectures playlist.