**Last updated:** 2019-03-31

**Checks:** 2 0

**Knit directory:** `fiveMinuteStats/analysis/`

This reproducible R Markdown analysis was created with workflowr (version 1.2.0). The *Report* tab describes the reproducibility checks that were applied when the results were created. The *Past versions* tab lists the development history.

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.

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:

```
Ignored files:
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: analysis/.Rhistory
Ignored: analysis/bernoulli_poisson_process_cache/
Untracked files:
Untracked: _workflowr.yml
Untracked: analysis/CI.Rmd
Untracked: analysis/gibbs_structure.Rmd
Untracked: analysis/libs/
Untracked: analysis/results.Rmd
Untracked: analysis/shiny/tester/
Untracked: docs/MH_intro_files/
Untracked: docs/citations.bib
Untracked: docs/figure/MH_intro.Rmd/
Untracked: docs/hmm_files/
Untracked: docs/libs/
Untracked: docs/shiny/tester/
```

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 R Markdown and HTML files. If you’ve configured a remote Git repository (see `?wflow_git_remote`

), click on the hyperlinks in the table below to view them.

File | Version | Author | Date | Message |
---|---|---|---|---|

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). |

html | fb0f6e3 | stephens999 | 2017-03-03 | Merge pull request #33 from mdavy86/f/review |

Rmd | d674141 | Marcus Davy | 2017-02-27 | typos, refs |

Rmd | 02d2d36 | stephens999 | 2017-02-20 | add shiny binomial example |

html | 02d2d36 | stephens999 | 2017-02-20 | add shiny binomial example |

This vignette introduces the idea of “conjugate prior” distributions for Bayesian inference for a continuous parameter. You should be familiar with Bayesian inference for a binomial proportion.

In this example we considered the following problem.

Suppose we sample 100 elephants from a population, and measure their DNA at a location in their genome (“locus”) where there are two types (“alleles”), which it is convenient to label 0 and 1.

In my sample, I observe that 30 of the elephants have the “1” allele and 70 have the “0” allele. What can I say about the frequency, \(q\), of the “1” allele in the population?

The example showed how to compute the posterior distribution for \(q\), using a *uniform prior distribution*. We saw that, conveniently, the posterior distribution for \(q\) is a Beta distribution.

Here we generalize this calculation to the case where the prior distribution on \(q\) is a Beta distribution. We will find that, in this case, the posterior distribution on \(q\) is again a Beta distribution. The property where the posterior distribution comes from the same family as the prior distribution is very convenient, and so has a special name: it is called “conjugacy”. We say “The Beta distribution is the conjugate prior distribution for the binomial proportion”.

As before we use Bayes Theorem which we can write in words as \[\text{posterior} \propto \text{likelihood} \times \text{prior},\] or in mathematical notation as \[ p(q | D) \propto p(D | q) p(q),\] where \(D\) denotes the observed data.

In this case, the likelihood \(p(D | q)\) is given by \[p(D | q) \propto q^{30} (1-q)^{70}\]

If our prior distribution on \(q\) is a Beta distribution, say Beta\((a,b)\), then the prior density \(p(q)\) is \[p(q) \propto q^{a-1}(1-q)^{b-1} \qquad (q \in [0,1]).\]

Combining these two we get: \[p(q | D) \propto q^{30} (1-q)^{70} q^{a-1} (1-q)^{b-1}\\ \propto q^{30+a-1}(1-q)^{70+b-1}\]

At this point we again apply the “trick” of recognizing this density as the density of a Beta distribution - specifically, the Beta distribution with parameters \((30+a,70+b)\).

Of course, there is nothing special about the 30 “1” alleles and 70 “0” alleles we observed here. Suppose we observed \(n_1\) of the “1” allele and \(n_0\) of the “0” allele. Then the likelihood becomes \[p(D | q) \propto q^{n_1} (1-q)^{n_0},\] and you should be able to show (Exercise) that the posterior is \[q|D \sim \text{Beta}(n_1+a, n_0+b).\]

When doing Bayesian inference for a binomial proportion, \(q\), if the prior distribution is a Beta distribution then the posterior distribution is also Beta.

We say “the Beta distribution is the conjugate prior for a binomial proportion”.

Show that the Gamma distribution is the conjugate prior for a Poisson mean.

That is, suppose we have observations \(X\) that are Poisson distributed, \(X \sim Poi(\mu)\). Assume that your prior distribution on \(\mu\) is a Gamma distribution with parameters \(n\) and \(\lambda\). Show that the posterior distribution on \(\mu\) is also a Gamma distribution.

Hint: you should take the following steps. 1. write down the likelihood \(p(X|\mu)\) for \(\mu\) (look up the Poisson distribution if you cannot remember it). 2. Write down the prior density for \(\mu\) (look up the density of a Gamma distribution if you cannot remember it). 3. Multiply them together to obtain the posterior density (up to a constant of proportionality), and notice that it has the same form as the gamma distribution.

This site was created with R Markdown