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+ <img src="../logo.svg" class="logo" alt=""><h1>Sequential Bayes: Utilizing the Posterior Distribution</h1>
+
+
+ <small class="dont-index">Source: <a href="https://github.com/MI2YorkU/Rnaught/blob/master/vignettes/seq_bayes_post.Rmd" class="external-link"><code>vignettes/seq_bayes_post.Rmd</code></a></small>
+ <div class="d-none name"><code>seq_bayes_post.Rmd</code></div>
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+<p>In the Sequential Bayes method, the probability distribution of R0 is
+updated sequentially from one case count to the next, starting from a
+(discretized) uniform prior. By default, the function
+<code>seq_bayes</code> returns the mean of the last updated posterior
+distribution as its estimate of R0. However, by setting the parameter
+<code>post</code> to <code>TRUE</code>, it is possible to return the
+final distribution itself:</p>
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Daily case counts.</span></span>
+<span><span class="va">cases</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">4</span>, <span class="fl">10</span>, <span class="fl">5</span>, <span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">19</span>, <span class="fl">3</span>, <span class="fl">3</span>, <span class="fl">14</span>, <span class="fl">4</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">posterior</span> <span class="op"><-</span> <span class="fu"><a href="../reference/seq_bayes.html">seq_bayes</a></span><span class="op">(</span><span class="va">cases</span>, mu <span class="op">=</span> <span class="fl">8</span>, kappa <span class="op">=</span> <span class="fl">7</span>, post <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
+<p>First, the distribution can be used to retrieve the original estimate
+(had <code>post</code> been left to its default value of
+<code>FALSE</code>) by calculating its mean:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># `supp` is the support of the distribution, and `pmf` is its probability mass</span></span>
+<span><span class="co"># function.</span></span>
+<span><span class="va">post_mean</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">posterior</span><span class="op">$</span><span class="va">supp</span> <span class="op">*</span> <span class="va">posterior</span><span class="op">$</span><span class="va">pmf</span><span class="op">)</span></span>
+<span><span class="va">post_mean</span></span>
+<span><span class="co">#> [1] 1.476652</span></span>
+<span></span>
+<span><span class="co"># Verify that the following is true:</span></span>
+<span><span class="va">post_mean</span> <span class="op">==</span> <span class="fu"><a href="../reference/seq_bayes.html">seq_bayes</a></span><span class="op">(</span><span class="va">cases</span>, mu <span class="op">=</span> <span class="fl">8</span>, kappa <span class="op">=</span> <span class="fl">7</span><span class="op">)</span></span>
+<span><span class="co">#> [1] TRUE</span></span></code></pre></div>
+<p>Another use of the posterior is to obtain an alternative estimate of
+R0. For instance, the following extracts the posterior mode rather than
+the mean:</p>
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">post_mode</span> <span class="op"><-</span> <span class="va">posterior</span><span class="op">$</span><span class="va">supp</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/which.min.html" class="external-link">which.max</a></span><span class="op">(</span><span class="va">posterior</span><span class="op">$</span><span class="va">pmf</span><span class="op">)</span><span class="op">]</span></span>
+<span><span class="va">post_mode</span></span>
+<span><span class="co">#> [1] 1.36</span></span></code></pre></div>
+<p>Returning the posterior is suitable for visualization purposes. Below
+is a graph containing the uniform prior, final posterior distribution,
+posterior mean and posterior mode:</p>
+<p><img src="seq_bayes_post_files/figure-html/unnamed-chunk-5-1.png" width="1400"></p>
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