Last updated: 2026-01-09

Checks: 1 1

Knit directory: fiveMinuteStats/analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: uncommitted changes

The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

Repository version: ee39c95

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version ee39c95. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Unstaged changes:
    Modified:   analysis/index.Rmd
    Modified:   analysis/shiny_normal_example.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/shiny_normal_example.Rmd) and HTML (docs/shiny_normal_example.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd ee39c95 Peter Carbonetto 2026-01-09 Created pdf version of bayes_conjugate_normal_mean vignette.
Rmd c345a1a Nan Xiao 2024-03-03 Fix broken and moved URL
html 5f62ee6 Matthew Stephens 2019-03-31 Build site.
Rmd 0cd28bd Matthew Stephens 2019-03-31 workflowr::wflow_publish(all = TRUE)
html 34bcc51 John Blischak 2017-03-06 Build site.
Rmd 5fbc8b5 John Blischak 2017-03-06 Update workflowr project with wflow_update (version 0.4.0).
Rmd 391ba3c John Blischak 2017-03-06 Remove front and end matter of non-standard templates.
html 8e61683 Marcus Davy 2017-03-03 rendered html using wflow_build(all=TRUE)
html c3b365a John Blischak 2017-01-02 Build site.
Rmd 67a8575 John Blischak 2017-01-02 Use external chunk to set knitr chunk options.
Rmd 5ec12c7 John Blischak 2017-01-02 Use session-info chunk.
Rmd ec4fd2d Xiao Nan 2016-04-13 add shiny example for conjugate normal

This illustrates how the prior, likelihood, and posterior behave for inference for a normal mean (\(\mu\)) from normal-distributed data, with a conjugate prior on \(\mu\).

Specifically the prior on \(\mu\) is N(\(\mu_0\), \(\tau_0^2\)) [the dotted line in the interactive plot below], and the data is sampled from a normal distribution N(\(\mu\), \(\sigma^2\)), which gives the likelihood [black line]. Note that the likelihood is scaled so it fits nicely on the graph (remember, likelihoods only matter up to a constant, so you can scale them however is convenient).

Because the normal distribution is the conjugate prior for normal sampling, the posterior distribution is also a normal distribution, and is shown in red.

By Bayes theorem, we have \[ \text{Pr}(\mu \mid \mathbf{y}, \sigma^2) \propto \text{Pr}(\mathbf{y} \, | \, \mu, \sigma^2) \text{Pr}(\mu) \]

\[ \text{N}(\mu_1, \tau_1^2) = \text{N}(\mu, \sigma^2) \text{N}(\mu_0, \tau_0^2) \]

where the posterior mean:

\[ \mu_1 = \frac{\frac{\mu_0}{\tau_0^2} + \frac{n \bar{y}}{\sigma^2}}{\frac{1}{\tau_0^2} + \frac{n}{\sigma^2}} \]

and the posterior variance:

\[ \tau_1^2 = (\frac{1}{\tau_0^2} + \frac{n}{\sigma^2})^{-1} \]

An interactive app that shows how the posterior distribution will change when the prior and the (scaled) data likelihood changes:

The source code of the app can be found here.