Research Fellow in Astronomy, working at the Ruhr-University Bochum.

This website primarily functions as the host place for my "Introduction to Statistics for Astronomers and Physicists" Lecture Notes. For those interested, it also contains links to my academic curriculum vitae and relevant websites.

I am a Research Fellow in the German Centre for Cosmological Lensing (GCCL) at the Ruhr Universität Bochum (RUB), Germany. I undertook my PhD at the University of Western Australia, where I studied the growth and evolution of baryonic mass as a member of the Galaxy and Mass Assembly (GAMA) collaboration. Following my PhD, I worked for the Kilo Degree Survey (KiDS) at the Argelander Institute for Astronomy at the Universität Bonn, where I began my work within weak gravitational lensing. For the last 5 years my research has focused on weak lensing survey science, and particularly on optimisation of photometric image reduction and analysis methods, systematics mitigation, and statistical analyses. Beyond weak lensing, I am a keen astrostatistician, an animated astronomy outreach presenter, and an enthusiastic but nonetheless mediocre golfer.

At the RUB I teach an introductory course in statistics for students studying Physics and Astronomy. The course is designed to be accessible to students with little or no previous statistical training. I attempt to develop a new aspect of the course each semester, and welcome comments/feedback about the course and it's contents. Any comments can be added to my AstroStats github repository using the issues list.

The lecture notes are directly accessible here in two formats:

- 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. - 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.

- 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
- Astrostatistics: Lecture 1
- Astrostatistics: Lecture 2
- Astrostatistics: MH Example
- Astrostatistics: Lecture 3

- Astrostatistics: Lecture 1
- Astrostatistics: Lecture 2
- Astrostatistics: MH Example
- Astrostatistics: Lecture 3

- 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

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
```

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

Various academic and non-academic websites on which I am active and/or which are relevant to my career can be found below:

- Recorded Academic Presentations: Academic Talks
- Recorded Outreach Presentations: Outreach Talks
- Recorded Lectures: Lectures
- NASA ADS: AHWPublications
- ORCID: 0000-0001-7363-7932
- Researchgate: Dr Angus H. Wright
- Twitter: @AstroAngus
- German Centre for Cosmological Lensing: GCCL

My long-form Academic CV is available in PDF format here.