Repository for Angus H Wright's Introduction to Statistics for Astronomers and Physicists Lecture Materials. Includes 13 Lectures and one Summary Ringvorlesung.
To view the published version of the lecture notes in their proper HTML formats: visit my github.io webpage. There you can find the lecture notes in single-page HTML format and in HTML slide format (which are used when teaching).
The lecture notes are provided in viewable-on-github markdown format using the links below or by directly clicking on the various "markdown" (.md) files in the Md directory directly above this description. Note, however, that much of the long-form LaTeX equations are not well-compiled in the github format. For this reason you may prefer to:
- view/download the PDF lecture notes on github (see the "View PDFs" subsection below); or
- Download the individual HTML page notes and slides (see the "Download HTML notes subsection below); or
- clone the repository and compile the lecture notes on your own machine (see the "Compiling the lectures" subsection below).
Lecture 2d is an interactive data-mining exercise that can really only be used in the slidy format. It is designed as a choose-your-own-adventure, where choices (in the form of hyperlinks to different slides) take you through different outcomes. However, the Slidy and HTML-Page versions of the lecture notes are not directly viewable in the github repository. So, best to view them via my website](https://anguswright.github.io/). A direct link to the exercise is here.
To get the most out of the lectures, you really should view them in the intended HTML formats. This circumvents the cases where the LaTeX has failed to show nicely on GitHub markdown and allows you to view the animations/media of the lecture notes that you can't see in the PDF versions. If you don't want to read them online via my website, you can download them directly in the HTML_pages and HTML_slides folders above.
Direct links to the HTML page versions are here:
- 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
Direct links to the HTML slide versions are here:
- 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
For convenience, direct links to the view-on-github Lectures are provided here:
- 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
For cases where the LaTeX has failed to show nicely on GitHub markdown, you may prefer to refer to the PDF versions of the Lectures:
- 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
Additionally, the lectures can be downloaded and compiled into a range of formats using the knit.sh script in the Rmd folder. The use of the knit.sh script assumes that you have a functional R installation with both the rmarkdown and rmdformats packages installed. R can be easily installed via conda. The process for installing packages in R is described in the Lecture 0 section "Installing and Loading Libraries" here.
Available formats are:
- HTML: produces html notes in the rmdformats::downcute style
- PDF: produces PDF lecture notes
- Slidy: produces slides for live-lecturing
- github: produces the viewable-on-github markdown documents above