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algorithms/TemperedSMC.html

@@ -577,7 +577,7 @@ <h3>Sample with HMC<a class="headerlink" href="#sample-with-hmc" title="Link to
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 2.24 s, sys: 82.4 ms, total: 2.32 s
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 2.23 s, sys: 65 ms, total: 2.3 s
 Wall time: 2 s
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@@ -598,7 +598,7 @@ <h3>Sample with HMC<a class="headerlink" href="#sample-with-hmc" title="Link to
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+<img alt="../_images/4967e8e83ceac0e552dc08c760ecc121a68aecfa77ad396b48087798a7193ca7.png" src="../_images/4967e8e83ceac0e552dc08c760ecc121a68aecfa77ad396b48087798a7193ca7.png" />
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@@ -620,8 +620,8 @@ <h3>Sample with NUTS<a class="headerlink" href="#sample-with-nuts" title="Link t
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 44.9 s, sys: 132 ms, total: 45 s
-Wall time: 44.2 s
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 45.5 s, sys: 115 ms, total: 45.7 s
+Wall time: 44.9 s
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+<img alt="../_images/ca5797e2db0a433e946a3154b26ee695b59e374644ab7691c4b281a8b1f25e32.png" src="../_images/ca5797e2db0a433e946a3154b26ee695b59e374644ab7691c4b281a8b1f25e32.png" />
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algorithms/mclmc.html

@@ -687,8 +687,8 @@ <h2>Validate the choice of <span class="math notranslate nohighlight">\(\epsilon
 <h2>A more complex example<a class="headerlink" href="#a-more-complex-example" title="Link to this heading">#</a></h2>
 <p>We now consider a more complex model, of stock volatility.</p>
 <p>The returns <span class="math notranslate nohighlight">\(r_n\)</span> are modeled by a Student’s-t distribution whose scale (volatility) <span class="math notranslate nohighlight">\(R_n\)</span> is time varying and unknown. The prior for <span class="math notranslate nohighlight">\(\log R_n\)</span> is a Gaussian random walk, with an exponential distribution of the random walk step-size <span class="math notranslate nohighlight">\(\sigma\)</span>. An exponential prior is also taken for the Student’s-t degrees of freedom <span class="math notranslate nohighlight">\(\nu\)</span>. The generative process of the data is:</p>
-<div class="amsmath math notranslate nohighlight" id="equation-78142d13-3708-497a-bb52-7b4de0ea735b">
-<span class="eqno">(1)<a class="headerlink" href="#equation-78142d13-3708-497a-bb52-7b4de0ea735b" title="Permalink to this equation">#</a></span>\[\begin{align}
+<div class="amsmath math notranslate nohighlight" id="equation-d6feca22-0f22-4e98-a007-fb7cb7c93d44">
+<span class="eqno">(1)<a class="headerlink" href="#equation-d6feca22-0f22-4e98-a007-fb7cb7c93d44" title="Permalink to this equation">#</a></span>\[\begin{align}
     &amp;r_n / R_n \sim \text{Student's-t}(\nu) \qquad
     &amp;&amp;\nu \sim \text{Exp}(\lambda = 1/10) \\ \nonumber
     &amp;\log R_n \sim \mathcal{N}(\log R_{n-1}, \sigma) \qquad