build based on 3896595

Author: Documenter.jl

dev/.documenter-siteinfo.json

@@ -1 +1 @@
-{"documenter":{"julia_version":"1.11.8","generation_timestamp":"2026-01-28T11:42:26","documenter_version":"1.16.1"}}
+{"documenter":{"julia_version":"1.11.8","generation_timestamp":"2026-01-29T05:20:58","documenter_version":"1.16.1"}}

dev/API/approximatedistributions/index.html

@@ -49,4 +49,4 @@
     volume = {78},
     pages = {1--14},
     doi = {10.1080/00031305.2023.2249522},
-  }</code></pre><h2 id="Contributing"><a class="docs-heading-anchor" href="#Contributing">Contributing</a><a id="Contributing-1"></a><a class="docs-heading-anchor-permalink" href="#Contributing" title="Permalink"></a></h2><p>If you encounter a bug or have a suggestion, please consider <a href="https://github.com/msainsburydale/NeuralEstimators.jl/issues">opening an issue</a> or submitting a pull request. Instructions for contributing to the documentation can be found in <a href="https://github.com/msainsburydale/NeuralEstimators.jl/tree/main/docs/README.md">docs/README.md</a>. When adding functionality to the package, you may wish to add unit tests to the file <a href="https://github.com/msainsburydale/NeuralEstimators.jl/tree/main/test/runtests.jl">test/runtests.jl</a>. You can then run these tests locally by executing the following command from the root folder:</p><pre><code class="language-bash hljs">julia --project=. -e &quot;using Pkg; Pkg.test()&quot;</code></pre><h2 id="Papers-using-NeuralEstimators"><a class="docs-heading-anchor" href="#Papers-using-NeuralEstimators">Papers using NeuralEstimators</a><a id="Papers-using-NeuralEstimators-1"></a><a class="docs-heading-anchor-permalink" href="#Papers-using-NeuralEstimators" title="Permalink"></a></h2><ul><li><p><strong>Likelihood-free parameter estimation with neural Bayes estimators</strong> <a href="https://doi.org/10.1080/00031305.2023.2249522">[paper]</a> <a href="https://github.com/msainsburydale/NeuralBayesEstimators">[code]</a></p></li><li><p><strong>Neural methods for amortized inference</strong> <a href="https://doi.org/10.1146/annurev-statistics-112723-034123">[paper]</a><a href="https://github.com/andrewzm/Amortised_Neural_Inference_Review">[code]</a></p></li><li><p><strong>Neural Bayes estimators for irregular spatial data using graph neural networks</strong> <a href="https://doi.org/10.1080/10618600.2024.2433671">[paper]</a><a href="https://github.com/msainsburydale/NeuralEstimatorsGNN">[code]</a></p></li><li><p><strong>Neural Bayes estimators for censored inference with peaks-over-threshold models</strong> <a href="https://jmlr.org/papers/v25/23-1134.html">[paper]</a> <a href="https://github.com/Jbrich95/CensoredNeuralEstimators">[code]</a></p></li><li><p><strong>Neural parameter estimation with incomplete data</strong> <a href="https://arxiv.org/abs/2501.04330">[paper]</a><a href="https://github.com/msainsburydale/NeuralIncompleteData">[code]</a></p></li><li><p><strong>Neural Bayes inference for complex bivariate extremal dependence models</strong> <a href="https://arxiv.org/abs/2503.23156">[paper]</a><a href="https://github.com/lidiamandre/NBE_classifier_depmodels">[code]</a></p></li><li><p><strong>Fast likelihood-free parameter estimation for Lévy processes</strong> <a href="https://www.arxiv.org/abs/2505.01639">[paper]</a></p></li></ul></article><nav class="docs-footer"><a class="docs-footer-nextpage" href="methodology/">Methodology »</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="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.16.1 on <span class="colophon-date" title="Wednesday 28 January 2026 11:42">Wednesday 28 January 2026</span>. Using Julia version 1.11.8.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+  }</code></pre><h2 id="Contributing"><a class="docs-heading-anchor" href="#Contributing">Contributing</a><a id="Contributing-1"></a><a class="docs-heading-anchor-permalink" href="#Contributing" title="Permalink"></a></h2><p>If you encounter a bug or have a suggestion, please consider <a href="https://github.com/msainsburydale/NeuralEstimators.jl/issues">opening an issue</a> or submitting a pull request. Instructions for contributing to the documentation can be found in <a href="https://github.com/msainsburydale/NeuralEstimators.jl/tree/main/docs/README.md">docs/README.md</a>. When adding functionality to the package, you may wish to add unit tests to the file <a href="https://github.com/msainsburydale/NeuralEstimators.jl/tree/main/test/runtests.jl">test/runtests.jl</a>. You can then run these tests locally by executing the following command from the root folder:</p><pre><code class="language-bash hljs">julia --project=. -e &quot;using Pkg; Pkg.test()&quot;</code></pre><h2 id="Papers-using-NeuralEstimators"><a class="docs-heading-anchor" href="#Papers-using-NeuralEstimators">Papers using NeuralEstimators</a><a id="Papers-using-NeuralEstimators-1"></a><a class="docs-heading-anchor-permalink" href="#Papers-using-NeuralEstimators" title="Permalink"></a></h2><ul><li><p><strong>Likelihood-free parameter estimation with neural Bayes estimators</strong> <a href="https://doi.org/10.1080/00031305.2023.2249522">[paper]</a> <a href="https://github.com/msainsburydale/NeuralBayesEstimators">[code]</a></p></li><li><p><strong>Neural methods for amortized inference</strong> <a href="https://doi.org/10.1146/annurev-statistics-112723-034123">[paper]</a><a href="https://github.com/andrewzm/Amortised_Neural_Inference_Review">[code]</a></p></li><li><p><strong>Neural Bayes estimators for irregular spatial data using graph neural networks</strong> <a href="https://doi.org/10.1080/10618600.2024.2433671">[paper]</a><a href="https://github.com/msainsburydale/NeuralEstimatorsGNN">[code]</a></p></li><li><p><strong>Neural Bayes estimators for censored inference with peaks-over-threshold models</strong> <a href="https://jmlr.org/papers/v25/23-1134.html">[paper]</a> <a href="https://github.com/Jbrich95/CensoredNeuralEstimators">[code]</a></p></li><li><p><strong>Neural parameter estimation with incomplete data</strong> <a href="https://arxiv.org/abs/2501.04330">[paper]</a><a href="https://github.com/msainsburydale/NeuralIncompleteData">[code]</a></p></li><li><p><strong>Neural Bayes inference for complex bivariate extremal dependence models</strong> <a href="https://arxiv.org/abs/2503.23156">[paper]</a><a href="https://github.com/lidiamandre/NBE_classifier_depmodels">[code]</a></p></li><li><p><strong>Fast likelihood-free parameter estimation for Lévy processes</strong> <a href="https://www.arxiv.org/abs/2505.01639">[paper]</a></p></li></ul></article><nav class="docs-footer"><a class="docs-footer-nextpage" href="methodology/">Methodology »</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="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.16.1 on <span class="colophon-date" title="Thursday 29 January 2026 05:20">Thursday 29 January 2026</span>. Using Julia version 1.11.8.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>