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‎developers/inference/abstractmcmc-turing/index.html‎

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<div class="cell-output cell-output-display" data-execution_count="1">
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<pre><code>Chains MCMC chain (1000×5×1 Array{Float64, 3}):
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parameters = s², m
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internals = lp, logprior, loglikelihood
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<pre><code>Chains MCMC chain (1000×5×1 Array{Float64, 3}):
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p\left(\theta_{\text{prop}}, x_{\text{obs}}\right)
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\]</span></p>
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<p>with <span class="math inline">\(\theta_{\text{prop}}\)</span> a sample from the proposal and <span class="math inline">\(x_{\text{obs}}\)</span> the observed data.</p>
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<p>This begs the question: how can these functions access model information during sampling? Recall that the model is stored as an instance <code>m</code> of <code>Model</code>. One of the attributes of <code>m</code> is the model evaluation function <code>m.f</code>, which is built by compiling the <code>@model</code> macro. Executing <code>f</code> runs the tilde statements of the model in order, and adds model information to the sampler (the instance of <code>Sampler</code> that stores information about the ongoing sampling process) at each step (see <a href="https://turinglang.org/dev/docs/for-developers/compiler">here</a> for more information about how the <code>@model</code> macro is compiled). The DynamicPPL functions <code>assume</code> and <code>observe</code> determine what kind of information to add to the sampler for every tilde statement.</p>
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<p>This begs the question: how can these functions access model information during sampling? Recall that the model is stored as an instance <code>m</code> of <code>Model</code>. One of the attributes of <code>m</code> is the model evaluation function <code>m.f</code>, which is built by compiling the <code>@model</code> macro. Executing <code>f</code> runs the tilde statements of the model in order, and adds model information to the sampler (the instance of <code>Sampler</code> that stores information about the ongoing sampling process) at each step (see <a href="../../../developers/compiler/model-manual">here</a> for more information about how the <code>@model</code> macro is compiled). The DynamicPPL functions <code>assume</code> and <code>observe</code> determine what kind of information to add to the sampler for every tilde statement.</p>
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<p>Consider an instance <code>m</code> of <code>Model</code> and a sampler <code>spl</code>, with associated <code>VarInfo</code> <code>vi = spl.state.vi</code>. At some point during the sampling process, an AbstractMCMC function such as <code>step!</code> calls <code>m(vi, ...)</code>, which calls the model evaluation function <code>m.f(vi, ...)</code>.</p>
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<ul>
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<li><p>for every tilde statement in the <code>@model</code> macro, <code>m.f(vi, ...)</code> returns model-related information (samples, value of the model density, etc.), and adds it to <code>vi</code>. How does it do that?</p>

‎faq/index.html‎

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<li><strong>Multithreaded sampling</strong>: Use <code>MCMCThreads()</code> to run one chain per thread</li>
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<li><strong>Distributed sampling</strong>: Use <code>MCMCDistributed()</code> for distributed computing</li>
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<p>See the <a href="../core-functionality/#sampling-multiple-chains">Core Functionality guide</a> for examples.</p>
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<p>See the <a href="../core-functionality#sampling-multiple-chains">Core Functionality guide</a> for examples.</p>
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</section>
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<section id="threading-within-models" class="level3">
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<h3 class="anchored" data-anchor-id="threading-within-models">2. Threading Within Models</h3>

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‎tutorials/bayesian-logistic-regression/index.html‎

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internals = 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, lp, logprior, loglikelihood
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<p>The <code>sample()</code> call above assumes that you have at least <code>nchains</code> threads available in your Julia instance. If you do not, the multiple chains will run sequentially, and you may notice a warning. For more information, see <a href="https://turinglang.org/dev/docs/using-turing/guide/#sampling-multiple-chains">the Turing documentation on sampling multiple chains.</a></p>
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<p>The <code>sample()</code> call above assumes that you have at least <code>nchains</code> threads available in your Julia instance. If you do not, the multiple chains will run sequentially, and you may notice a warning. For more information, see <a href="../../core-functionality#sampling-multiple-chains">the Turing documentation on sampling multiple chains.</a></p>
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<p>The <code>sample()</code> call above assumes that you have at least <code>nchains</code> threads available in your Julia instance. If you do not, the multiple chains will run sequentially, and you may notice a warning. For more information, see <a href="https://turinglang.org/dev/docs/using-turing/guide/#sampling-multiple-chains">the Turing documentation on sampling multiple chains.</a></p>
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<p>The <code>sample()</code> call above assumes that you have at least <code>nchains</code> threads available in your Julia instance. If you do not, the multiple chains will run sequentially, and you may notice a warning. For more information, see <a href="../../core-functionality#sampling-multiple-chains">the Turing documentation on sampling multiple chains.</a></p>
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@@ -1102,8 +1102,8 @@ <h1>Removing the Warmup Samples</h1>
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Iterations = 201:1:2500
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Number of chains = 4
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Samples per chain = 2300
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Wall duration = 20.85 seconds
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Compute duration = 17.45 seconds
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Wall duration = 21.64 seconds
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Compute duration = 18.04 seconds
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parameters = b0, b1, b2, b3
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internals = 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, lp, logprior, loglikelihood
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