Uploaded January 2019 | Updated September 2026, 1 hour ago
This video introduces an app called 'The distribution zoo' to help understand and apply statistical distributions in research. This app (available here: ben18785.shinyapps.io/distribution-zoo/) allows a user to do the following:
- Dynamically change the parameters of 24 distributions, ranging from fairly simple cases (for example, normal or Poisson), up to more complex cases such as the LKJ correlation distribution, and investigate how these parameters affect the resultant distributional properties, including plots of the probability density function or probability mass function, histograms of the sampling distribution for some cases where the PDFs are hard to visualise, and cumulative distribution functions where appropriate.
- The app contains dynamic code snippets in R, Python, Matlab, Mathematica, Julia and Stan, which reflect changes in the parameter values. Importantly, for each distribution and parameter set, these functions should give equivalent results across all five languages. For me, this is really useful, since different languages have different parameterisations and some (Python!) have really non-standard parameterisations, which can make translating code from one language to another tricky.
- The app contains detailed and vetted formulae for each distribution giving useful properties. These formulae are also available in LaTeX form to make using them in articles, reports and so on simpler.
Because it’s been deployed to the web, you can use this app anywhere. Look at the zoo of distributions on your laptop, tablet or smartphone!
This video is part of a lecture course which closely follows the material covered in the book, "A Student's Guide to Bayesian Statistics", published by Sage, which is available to order on Amazon here: amazon.co.uk/Students-Guide-Bayesian-Statistics/dp/1473916364
For more information on all things Bayesian, have a look at: ben-lambert.com/bayesian/. The playlist for the lecture course is here: youtube.com/playlist?list=PLwJRxp3blEvZ8AKMXOy0fc0cqT61GsKCG&disable_polymer=true
The Github page for the app's development is here: github.com/ben18785/distribution-zoo
This video introduces an app called 'The distribution zoo' to help understand and apply statistical distributions in research. This app (available here: ben18785.shinyapps.io/distribution-zoo/) allows a user to do the following:
- Dynamically change the parameters of 24 distributions, ranging from fairly simple cases (for example, normal or Poisson), up to more complex cases such as the LKJ correlation distribution, and investigate how these parameters affect the resultant distributional properties, including plots of the probability density function or probability mass function, histograms of the sampling distribution for some cases where the PDFs are hard to visualise, and cumulative distribution functions where appropriate.
- The app contains dynamic code snippets in R, Python, Matlab, Mathematica, Julia and Stan, which reflect changes in the parameter values. Importantly, for each distribution and parameter set, these functions should give equivalent results across all five languages. For me, this is really useful, since different languages have different parameterisations and some (Python!) have really non-standard parameterisations, which can make translating code from one language to another tricky.
- The app contains detailed and vetted formulae for each distribution giving useful properties. These formulae are also available in LaTeX form to make using them in articles, reports and so on simpler.
Because it’s been deployed to the web, you can use this app anywhere. Look at the zoo of distributions on your laptop, tablet or smartphone!
This video is part of a lecture course which closely follows the material covered in the book, "A Student's Guide to Bayesian Statistics", published by Sage, which is available to order on Amazon here: amazon.co.uk/Students-Guide-Bayesian-Statistics/dp/1473916364
For more information on all things Bayesian, have a look at: ben-lambert.com/bayesian/. The playlist for the lecture course is here: youtube.com/playlist?list=PLwJRxp3blEvZ8AKMXOy0fc0cqT61GsKCG&disable_polymer=true
The Github page for the app's development is here: github.com/ben18785/distribution-zoo
