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-{"documenter":{"julia_version":"1.9.4","generation_timestamp":"2024-02-05T02:15:54","documenter_version":"1.2.1"}}
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dev/api/data_fitting_calibration/index.html

@@ -7,18 +7,18 @@
 ]</code></pre><p>then if <code>datafit(prob, p, t, data)</code>, <code>t</code> must be length 3 and these values correspond to <code>x(t[i])</code>.</p><p>If <code>datafit(prob, p, data)</code>, then the data must be a tuple of (t, timeseries), for example:</p><pre><code class="language-julia hljs">[
 x =&gt; ([1.0, 2.0, 3.0], [11.352378507900013, 11.818374125301172, -10.72999081810307])
 z =&gt; ([0.5, 1.5, 2.5, 3.5], [2.005502877055581, 13.626953144513832, 5.382984515620634, 12.232084518374545])
-]</code></pre><p>where this means x(2.0) == 11.81...</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/SciML/EasyModelAnalysis.jl/blob/2f2bbd1ff117fa7fd0baa9e3ca6153aa2702a0a4/src/datafit.jl#L56-L103">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="EasyModelAnalysis.global_datafit" href="#EasyModelAnalysis.global_datafit"><code>EasyModelAnalysis.global_datafit</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia hljs">global_datafit(prob, pbounds, t, data; maxiters = 10000)
+]</code></pre><p>where this means x(2.0) == 11.81...</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/SciML/EasyModelAnalysis.jl/blob/dc16d9f8b89c222a2e6931aa233ef569ab5aad9f/src/datafit.jl#L56-L103">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="EasyModelAnalysis.global_datafit" href="#EasyModelAnalysis.global_datafit"><code>EasyModelAnalysis.global_datafit</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia hljs">global_datafit(prob, pbounds, t, data; maxiters = 10000)
 global_datafit(prob, pbounds, data; maxiters = 10000)</code></pre><p>Fit parameters <code>p</code> to <code>data</code> measured at times <code>t</code>.</p><p><strong>Arguments</strong></p><ul><li><code>prob</code>: ODEProblem</li><li><code>pbounds</code>: Vector of pairs of symbolic parameters to vectors of lower and upper bounds for the parameters.</li><li><code>t</code>: Vector of time-points</li><li><code>data</code>: Vector of pairs of symbolic states and measurements of these states at times <code>t</code>.</li></ul><p><strong>Keyword Arguments</strong></p><ul><li><code>maxiters</code>: how long to run the optimization for. Defaults to 10000. Larger values are slower but more robust.</li><li><code>loss</code>: the loss function used for fitting. Defaults to <code>EasyModelAnalysis.l2loss</code>, with an alternative being <code>EasyModelAnalysis.relative_l2loss</code> for relative weighted error.</li></ul><p><code>p</code> does not have to contain all the parameters required to solve <code>prob</code>, it can be a subset of parameters. Other parameters necessary to solve <code>prob</code> default to the parameter values found in <code>prob.p</code>. Similarly, not all states must be measured.</p><p><strong>Data Definition</strong></p><p>The data definition is given as a vctor of pairs. If <code>t</code> is specified globally for the datafit, then those time series correspond to the time points specified. For example, </p><pre><code class="language-julia hljs">[
 x =&gt; [11.352378507900013, 11.818374125301172, -10.72999081810307]
 z =&gt; [2.005502877055581, 13.626953144513832, 5.382984515620634, 12.232084518374545]
 ]</code></pre><p>then if <code>datafit(prob, p, t, data)</code>, <code>t</code> must be length 3 and these values correspond to <code>x(t[i])</code>.</p><p>If <code>datafit(prob, p, data)</code>, then the data must be a tuple of (t, timeseries), for example:</p><pre><code class="language-julia hljs">[
 x =&gt; ([1.0, 2.0, 3.0], [11.352378507900013, 11.818374125301172, -10.72999081810307])
 z =&gt; ([0.5, 1.5, 2.5, 3.5], [2.005502877055581, 13.626953144513832, 5.382984515620634, 12.232084518374545])
-]</code></pre><p>where this means x(2.0) == 11.81...</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/SciML/EasyModelAnalysis.jl/blob/2f2bbd1ff117fa7fd0baa9e3ca6153aa2702a0a4/src/datafit.jl#L125-L174">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="EasyModelAnalysis.bayesian_datafit" href="#EasyModelAnalysis.bayesian_datafit"><code>EasyModelAnalysis.bayesian_datafit</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia hljs">bayesian_datafit(prob, p, t, data)
+]</code></pre><p>where this means x(2.0) == 11.81...</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/SciML/EasyModelAnalysis.jl/blob/dc16d9f8b89c222a2e6931aa233ef569ab5aad9f/src/datafit.jl#L125-L174">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="EasyModelAnalysis.bayesian_datafit" href="#EasyModelAnalysis.bayesian_datafit"><code>EasyModelAnalysis.bayesian_datafit</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia hljs">bayesian_datafit(prob, p, t, data)
 bayesian_datafit(prob, p, data)</code></pre><p>Calculate posterior distribution for parameters <code>p</code> given <code>data</code> measured at times <code>t</code>.</p><p><strong>Data Definition</strong></p><p>The data definition is given as a vctor of pairs. If <code>t</code> is specified globally for the datafit, then those time series correspond to the time points specified. For example, </p><pre><code class="language-julia hljs">[
 x =&gt; [11.352378507900013, 11.818374125301172, -10.72999081810307]
 z =&gt; [2.005502877055581, 13.626953144513832, 5.382984515620634, 12.232084518374545]
 ]</code></pre><p>then if <code>datafit(prob, p, t, data)</code>, <code>t</code> must be length 3 and these values correspond to <code>x(t[i])</code>.</p><p>If <code>datafit(prob, p, data)</code>, then the data must be a tuple of (t, timeseries), for example:</p><pre><code class="language-julia hljs">[
 x =&gt; ([1.0, 2.0, 3.0], [11.352378507900013, 11.818374125301172, -10.72999081810307])
 z =&gt; ([0.5, 1.5, 2.5, 3.5], [2.005502877055581, 13.626953144513832, 5.382984515620634, 12.232084518374545])
-]</code></pre><p>where this means x(2.0) == 11.81...</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/SciML/EasyModelAnalysis.jl/blob/2f2bbd1ff117fa7fd0baa9e3ca6153aa2702a0a4/src/datafit.jl#L252-L282">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../basic_queries/">« Basic Queries</a><a class="docs-footer-nextpage" href="../sensitivity_analysis/">Sensitivity Analysis »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="auto">Automatic (OS)</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.2.1 on <span class="colophon-date" title="Monday 5 February 2024 02:15">Monday 5 February 2024</span>. Using Julia version 1.9.4.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+]</code></pre><p>where this means x(2.0) == 11.81...</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/SciML/EasyModelAnalysis.jl/blob/dc16d9f8b89c222a2e6931aa233ef569ab5aad9f/src/datafit.jl#L252-L282">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../basic_queries/">« Basic Queries</a><a class="docs-footer-nextpage" href="../sensitivity_analysis/">Sensitivity Analysis »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="auto">Automatic (OS)</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.2.1 on <span class="colophon-date" title="Monday 5 February 2024 02:16">Monday 5 February 2024</span>. Using Julia version 1.9.4.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>