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@@ -739,7 +739,7 @@ <h2 class="anchored" data-anchor-id="metadata"><code>Metadata</code></h2>
 <li><code>md.idcs</code> is the dictionary that maps each <code>VarName</code> instance to its index in <code>md.vns</code>, <code>md.ranges</code>, <code>md.dists</code>, <code>md.orders</code> and <code>md.flags</code>.</li>
 <li><code>md.vns[md.idcs[vn]] == vn</code>.</li>
 <li><code>md.dists[md.idcs[vn]]</code> is the distribution of <code>vn</code>.</li>
-<li><code>md.gids[md.idcs[vn]]</code> is the set of algorithms used to sample <code>vn</code>. This is used in the Gibbs sampling process.</li>
+<li><code>md.gids[md.idcs[vn]]</code> is the set of algorithms used to sample <code>vn</code>. This was used by the Gibbs sampler. Since Turing v0.36 it is unused and will eventually be deleted.</li>
 <li><code>md.orders[md.idcs[vn]]</code> is the number of <code>observe</code> statements before <code>vn</code> is sampled.</li>
 <li><code>md.ranges[md.idcs[vn]]</code> is the index range of <code>vn</code> in <code>md.vals</code>.</li>
 <li><code>md.vals[md.ranges[md.idcs[vn]]]</code> is the linearized vector of values of corresponding to <code>vn</code>.</li>

diff --git a/developers/compiler/minituring-compiler/index.html b/developers/compiler/minituring-compiler/index.html
@@ -76,8 +76,8 @@
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@@ -190,10 +190,10 @@
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@@ -761,16 +761,16 @@ <h1>Consider a probabilistic model defined by</h1>
   parameters      mean       std      mcse      ess_bulk      ess_tail      rh ⋯
       Symbol   Float64   Float64   Float64       Float64       Float64   Float ⋯
 
-           a    0.9771    0.8977    0.0031    83138.7250   121471.3060    1.00 ⋯
-           b    2.8808    0.4890    0.0012   171437.4763   214293.9000    1.00 ⋯
+           a    0.9811    0.8994    0.0032    79824.8778   119042.5191    1.00 ⋯
+           b    2.8813    0.4881    0.0012   171981.0044   210069.8438    1.00 ⋯
                                                                2 columns omitted
 
 Quantiles
   parameters      2.5%     25.0%     50.0%     75.0%     97.5%
       Symbol   Float64   Float64   Float64   Float64   Float64
 
-           a   -0.7765    0.3724    0.9756    1.5817    2.7425
-           b    1.9206    2.5516    2.8812    3.2103    3.8394</code></pre>
+           a   -0.7862    0.3763    0.9809    1.5870    2.7400
+           b    1.9244    2.5526    2.8825    3.2092    3.8371</code></pre>
 </div>
 </div>
 <p>We compare these results with Turing.</p>
@@ -792,25 +792,25 @@ <h1>Consider a probabilistic model defined by</h1>
 Iterations        = 1:1:1000000
 Number of chains  = 1
 Samples per chain = 1000000
-Wall duration     = 30.9 seconds
-Compute duration  = 30.9 seconds
+Wall duration     = 31.49 seconds
+Compute duration  = 31.49 seconds
 parameters        = a, b
 internals         = lp
 
 Summary Statistics
   parameters      mean       std      mcse      ess_bulk      ess_tail      rh ⋯
       Symbol   Float64   Float64   Float64       Float64       Float64   Float ⋯
 
-           a    0.9747    0.8991    0.0032    80991.8672   124482.5382    1.00 ⋯
-           b    2.8809    0.4883    0.0012   171713.6363   217545.2969    1.00 ⋯
+           a    0.9743    0.9040    0.0032    81189.5129   121269.8584    1.00 ⋯
+           b    2.8811    0.4891    0.0012   174601.6868   216651.2495    1.00 ⋯
                                                                2 columns omitted
 
 Quantiles
   parameters      2.5%     25.0%     50.0%     75.0%     97.5%
       Symbol   Float64   Float64   Float64   Float64   Float64
 
-           a   -0.7918    0.3694    0.9750    1.5803    2.7369
-           b    1.9255    2.5504    2.8809    3.2120    3.8349</code></pre>
+           a   -0.8004    0.3640    0.9756    1.5839    2.7366
+           b    1.9225    2.5506    2.8809    3.2119    3.8368</code></pre>
 </div>
 </div>
 <p>As you can see, with our simple probabilistic programming language and custom samplers we get similar results as Turing.</p>

diff --git a/developers/compiler/minituring-contexts/index.html b/developers/compiler/minituring-contexts/index.html
@@ -76,8 +76,8 @@
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@@ -161,10 +161,10 @@
 </ul>
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-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-v0.35" role="link" data-bs-toggle="dropdown" aria-expanded="false">
- <span class="menu-text">v0.35</span>
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@@ -777,16 +777,16 @@ <h2 class="anchored" data-anchor-id="contexts-within-contexts">Contexts within c
   parameters      mean       std      mcse      ess_bulk      ess_tail      rh ⋯
       Symbol   Float64   Float64   Float64       Float64       Float64   Float ⋯
 
-           a    0.9728    0.9011    0.0032    81189.5526   121519.1032    1.00 ⋯
-           b    2.8810    0.4880    0.0012   171599.0254   215249.0967    1.00 ⋯
+           a    0.9713    0.9014    0.0032    80387.2713   119459.5514    1.00 ⋯
+           b    2.8796    0.4892    0.0012   172170.0034   211488.7837    1.00 ⋯
                                                                2 columns omitted
 
 Quantiles
   parameters      2.5%     25.0%     50.0%     75.0%     97.5%
       Symbol   Float64   Float64   Float64   Float64   Float64
 
-           a   -0.7901    0.3647    0.9706    1.5798    2.7445
-           b    1.9254    2.5512    2.8796    3.2096    3.8379</code></pre>
+           a   -0.8075    0.3629    0.9732    1.5803    2.7342
+           b    1.9202    2.5498    2.8803    3.2090    3.8386</code></pre>
 </div>
 </div>
 <p>or we can choose to sample from the prior instead</p>
@@ -804,16 +804,16 @@ <h2 class="anchored" data-anchor-id="contexts-within-contexts">Contexts within c
   parameters      mean       std      mcse     ess_bulk      ess_tail      rha ⋯
       Symbol   Float64   Float64   Float64      Float64       Float64   Float6 ⋯
 
-           a    0.4970    0.9983    0.0040   62973.7057   122183.9030    1.000 ⋯
-           b    0.4793    2.2238    0.0135   27032.5061    52975.4949    1.000 ⋯
+           a    0.4977    1.0017    0.0040   64312.4640   127157.3129    1.000 ⋯
+           b    0.4929    2.2363    0.0135   27644.6544    51960.2825    1.000 ⋯
                                                                2 columns omitted
 
 Quantiles
   parameters      2.5%     25.0%     50.0%     75.0%     97.5%
       Symbol   Float64   Float64   Float64   Float64   Float64
 
-           a   -1.4634   -0.1761    0.4964    1.1686    2.4551
-           b   -3.8746   -1.0195    0.4793    1.9764    4.8342</code></pre>
+           a   -1.4669   -0.1768    0.4980    1.1717    2.4600
+           b   -3.8853   -1.0153    0.4988    2.0051    4.8568</code></pre>
 </div>
 </div>
 <p>Of course, using an MCMC algorithm to sample from the prior is unnecessary and silly (<code>PriorSampler</code> exists, after all), but the point is to illustrate the flexibility of the context system. We could, for instance, use the same setup to implement an <em>Approximate Bayesian Computation</em> (ABC) algorithm.</p>

diff --git a/developers/compiler/model-manual/index.html b/developers/compiler/model-manual/index.html
@@ -76,8 +76,8 @@
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@@ -161,10 +161,10 @@
 </ul>
             <ul class="navbar-nav navbar-nav-scroll ms-auto">
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-v0.35" role="link" data-bs-toggle="dropdown" aria-expanded="false">
- <span class="menu-text">v0.35</span>
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-v0.36" role="link" data-bs-toggle="dropdown" aria-expanded="false">
+ <span class="menu-text">v0.36</span>
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@@ -553,33 +553,33 @@ <h1 class="title">Manually Defining a Model</h1>
 <div class="sourceCode cell-code" id="cb5"><pre class="sourceCode julia code-with-copy"><code class="sourceCode julia"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>chain <span class="op">=</span> <span class="fu">sample</span>(model2, <span class="fu">NUTS</span>(), <span class="fl">1000</span>; progress<span class="op">=</span><span class="cn">false</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>┌ Info: Found initial step size
-└   ϵ = 2.25</code></pre>
+└   ϵ = 1.6</code></pre>
 </div>
 <div class="cell-output cell-output-display" data-execution_count="1">
 <pre><code>Chains MCMC chain (1000×14×1 Array{Float64, 3}):
 
 Iterations        = 501:1:1500
 Number of chains  = 1
 Samples per chain = 1000
-Wall duration     = 7.28 seconds
-Compute duration  = 7.28 seconds
+Wall duration     = 7.59 seconds
+Compute duration  = 7.59 seconds
 parameters        = s², m
 internals         = lp, n_steps, is_accept, acceptance_rate, log_density, hamiltonian_energy, hamiltonian_energy_error, max_hamiltonian_energy_error, tree_depth, numerical_error, step_size, nom_step_size
 
 Summary Statistics
   parameters      mean       std      mcse   ess_bulk   ess_tail      rhat   e ⋯
       Symbol   Float64   Float64   Float64    Float64    Float64   Float64     ⋯
 
-          s²    2.1230    1.8732    0.0953   542.9428   423.7377    1.0007     ⋯
-           m    1.2087    0.8148    0.0374   500.8337   502.9597    1.0032     ⋯
+          s²    2.3949    4.5577    0.2982   236.3625   313.1934    1.0074     ⋯
+           m    1.1687    0.9203    0.0525   353.5452   198.7300    1.0057     ⋯
                                                                 1 column omitted
 
 Quantiles
   parameters      2.5%     25.0%     50.0%     75.0%     97.5%
       Symbol   Float64   Float64   Float64   Float64   Float64
 
-          s²    0.5793    1.0428    1.5723    2.3921    7.2768
-           m   -0.3750    0.7304    1.2244    1.6654    2.8921</code></pre>
+          s²    0.5984    1.0413    1.5606    2.4078    7.7236
+           m   -0.9764    0.6446    1.2060    1.7290    3.0094</code></pre>
 </div>
 </div>
 <p>The subsequent pages in this section will show how the <code>@model</code> macro does this behind-the-scenes.</p>