<div id="documenter"><nav class="docs-sidebar"><div class="docs-package-name"><span class="docs-autofit"><a href="../../">Turing</a></span></div><button class="docs-search-query input is-rounded is-small is-clickable my-2 mx-auto py-1 px-2" id="documenter-search-query">Search docs (Ctrl + /)</button><ul class="docs-menu"><li><a class="tocitem" href="../../">Home</a></li><li><a class="tocitem" href="../">API</a></li><li><span class="tocitem">Submodule APIs</span><ul><li><a class="tocitem" href="../Inference/">Inference</a></li><li class="is-active"><a class="tocitem" href>Optimisation</a></li><li><a class="tocitem" href="../Variational/">Variational </a></li><li><a class="tocitem" href="../RandomMeasures/">RandomMeasures </a></li></ul></li></ul><div class="docs-version-selector field has-addons"><div class="control"><span class="docs-label button is-static is-size-7">Version</span></div><div class="docs-selector control is-expanded"><div class="select is-fullwidth is-size-7"><select 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id="documenter-settings-button" href="#" title="Settings"></a><a class="docs-article-toggle-button fa-solid fa-chevron-up" id="documenter-article-toggle-button" href="javascript:;" title="Collapse all docstrings"></a></div></header><article class="content" id="documenter-page"><h1 id="API:-Turing.Optimisation"><a class="docs-heading-anchor" href="#API:-Turing.Optimisation">API: <code>Turing.Optimisation</code></a><a id="API:-Turing.Optimisation-1"></a><a class="docs-heading-anchor-permalink" href="#API:-Turing.Optimisation" title="Permalink"></a></h1><article><details class="docstring" open="true"><summary id="DynamicPPL.InitFromParams-Tuple{Turing.Optimisation.ModeResult}"><a class="docstring-binding" href="#DynamicPPL.InitFromParams-Tuple{Turing.Optimisation.ModeResult}"><code>DynamicPPL.InitFromParams</code></a> — <span class="docstring-category">Method</span></summary><section><div><pre><code class="language-julia hljs">InitFromParams(m::ModeResult)</code></pre><p>Initialize a model from the parameters stored in a <code>ModeResult</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/TuringLang/Turing.jl/blob/4dc7ad096f92fd571de312e7751986484ac6cb50/src/optimisation/Optimisation.jl#L130-L134">source</a></section></details></article><article><details class="docstring" open="true"><summary id="SciMLBase.OptimizationProblem-Tuple{Turing.Optimisation.OptimLogDensity, AbstractVector, Any, Any}"><a class="docstring-binding" href="#SciMLBase.OptimizationProblem-Tuple{Turing.Optimisation.OptimLogDensity, AbstractVector, Any, Any}"><code>SciMLBase.OptimizationProblem</code></a> — <span class="docstring-category">Method</span></summary><section><div><pre><code class="language-julia hljs">OptimizationProblem(log_density::OptimLogDensity, initial_params::AbstractVector, adtype, constraints)</code></pre><p>Create an <code>OptimizationProblem</code> for the objective function defined by <code>log_density</code>.</p><p>Note that the adtype parameter here overrides any adtype parameter the OptimLogDensity was constructed with.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/TuringLang/Turing.jl/blob/4dc7ad096f92fd571de312e7751986484ac6cb50/src/optimisation/Optimisation.jl#L382-L389">source</a></section></details></article><article><details class="docstring" open="true"><summary id="Turing.Optimisation.MAP"><a class="docstring-binding" href="#Turing.Optimisation.MAP"><code>Turing.Optimisation.MAP</code></a> — <span class="docstring-category">Type</span></summary><section><div><pre><code class="language-julia hljs">MAP <: ModeEstimator</code></pre><p>Concrete type for maximum a posteriori estimation.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/TuringLang/Turing.jl/blob/4dc7ad096f92fd571de312e7751986484ac6cb50/src/optimisation/Optimisation.jl#L39-L43">source</a></section></details></article><article><details class="docstring" open="true"><summary id="Turing.Optimisation.MLE"><a class="docstring-binding" href="#Turing.Optimisation.MLE"><code>Turing.Optimisation.MLE</code></a> — <span class="docstring-category">Type</span></summary><section><div><pre><code class="language-julia hljs">MLE <: ModeEstimator</code></pre><p>Concrete type for maximum likelihood estimation.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/TuringLang/Turing.jl/blob/4dc7ad096f92fd571de312e7751986484ac6cb50/src/optimisation/Optimisation.jl#L32-L36">source</a></section></details></article><article><details class="docstring" open="true"><summary id="Turing.Optimisation.ModeEstimationConstraints"><a class="docstring-binding" href="#Turing.Optimisation.ModeEstimationConstraints"><code>Turing.Optimisation.ModeEstimationConstraints</code></a> — <span class="docstring-category">Type</span></summary><section><div><pre><code class="language-julia hljs">ModeEstimationConstraints</code></pre><p>A struct that holds constraints for mode estimation problems.</p><p>The fields are the same as possible constraints supported by the Optimization.jl: <code>ub</code> and <code>lb</code> specify lower and upper bounds of box constraints. <code>cons</code> is a function that takes the parameters of the model and returns a list of derived quantities, which are then constrained by the lower and upper bounds set by <code>lcons</code> and <code>ucons</code>. We refer to these as generic constraints. Please see the documentation of <a href="https://docs.sciml.ai/Optimization/stable/">Optimization.jl</a> for more details.</p><p>Any of the fields can be <code>nothing</code>, disabling the corresponding constraints.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/TuringLang/Turing.jl/blob/4dc7ad096f92fd571de312e7751986484ac6cb50/src/optimisation/Optimisation.jl#L310-L323">source</a></section></details></article><article><details class="docstring" open="true"><summary id="Turing.Optimisation.ModeEstimator"><a class="docstring-binding" href="#Turing.Optimisation.ModeEstimator"><code>Turing.Optimisation.ModeEstimator</code></a> — <span class="docstring-category">Type</span></summary><section><div><pre><code class="language-julia hljs">ModeEstimator</code></pre><p>An abstract type to mark whether mode estimation is to be done with maximum a posteriori (MAP) or maximum likelihood estimation (MLE).</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/TuringLang/Turing.jl/blob/4dc7ad096f92fd571de312e7751986484ac6cb50/src/optimisation/Optimisation.jl#L24-L29">source</a></section></details></article><article><details class="docstring" open="true"><summary id="Turing.Optimisation.ModeResult"><a class="docstring-binding" href="#Turing.Optimisation.ModeResult"><code>Turing.Optimisation.ModeResult</code></a> — <span class="docstring-category">Type</span></summary><section><div><pre><code class="language-julia hljs">ModeResult{
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