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---
layout: default
---
<div class="row">
<span class="selector">
<span>Authors: </span>
<select id="authorselection" class="selectpicker" multiple data-live-search="true">
<option>Aleksandra M. Walczak</option>
<option>Alex Cole</option>
<option>Alexander Fengler</option>
<option>Alexandre Gramfort</option>
<option>Alvaro Tejero-Cantero</option>
<option>Andreas Hellander</option>
<option>Andreas Voss</option>
<option>Andrew Phillips</option>
<option>Andriy Mnih</option>
<option>Antoine Wehenkel</option>
<option>Armita Nourmohammad</option>
<option>Arnaud Delaunoy</option>
<option>Arnaud Doucet</option>
<option>Arthur Gretton</option>
<option>Benjamin Kurt Miller</option>
<option>Bernhard Schölkopf</option>
<option>Carsten Rother</option>
<option>Christoph Weniger</option>
<option>Conor Durkan</option>
<option>Daniel Rahner</option>
<option>Daniel Ward</option>
<option>David C. Sterratt</option>
<option>David M. Blei</option>
<option>David S. Greenberg</option>
<option>Dustin Tran</option>
<option>Edward Meeds</option>
<option>Eric W. Pellegrini</option>
<option>Erich Schikuta</option>
<option>Francesco Nattino</option>
<option>François Rozet</option>
<option>Fredrik Wrede</option>
<option>Geoff Nicholls</option>
<option>Geoffrey Roeder</option>
<option>George Deligiannidis</option>
<option>George Papamakarios</option>
<option>Giacomo Bassetto</option>
<option>Gilles Louppe</option>
<option>Giulio Isacchini</option>
<option>Hanwen Xing</option>
<option>Iain Murray</option>
<option>Jack Simons</option>
<option>Jakob H. Macke</option>
<option>Jakob Kruse</option>
<option>Jan Boelts</option>
<option>Jan-Matthis Lueckmann</option>
<option>Jeong (Kate) Lee</option>
<option>Jes Frellsen</option>
<option>Joeri Hermans</option>
<option>Johann Brehmer</option>
<option>Jonathan Gair</option>
<option>Kaan Öcal</option>
<option>Konstantin T. Matchev</option>
<option>Kyle Cranmer</option>
<option>Kyoungchul Kong</option>
<option>Lakshmi N Govindarajan</option>
<option>Lena Maier-Hein</option>
<option>Linda Petzold</option>
<option>Lorenzo Pacchiardi</option>
<option>Louis Sharrock</option>
<option>Lynton Ardizzone</option>
<option>Manuel Glöckler</option>
<option>Marcel Nonnenmacher</option>
<option>Marco D'Alessandro</option>
<option>Mark Beaumont</option>
<option>Marvin Schmitt</option>
<option>Matteo Fasiolo</option>
<option>Maximilian Dax</option>
<option>Meiert W Grootes</option>
<option>Michael Arbel</option>
<option>Michael Deistler</option>
<option>Michael J Frank</option>
<option>Natanael Spisak</option>
<option>Neil Dalchau</option>
<option>Ou Ku</option>
<option>Patrick Cannon</option>
<option>Patrick Forré</option>
<option>Paul K. Grant</option>
<option>Paul-Christian Bürkner</option>
<option>Pedro J. Goncalves</option>
<option>Pedro J. Gonçalves</option>
<option>Pedro L. C. Rodrigues</option>
<option>Pierre Glaser</option>
<option>Poornima Ramesh</option>
<option>Prasanth Shyamsundar</option>
<option>Prashant Singh</option>
<option>Rajesh Ranganath</option>
<option>Ralf S. Klessen</option>
<option>Ramis Khabibullin</option>
<option>Richard Jiang</option>
<option>Ritabrata Dutta</option>
<option>Robin Eriksson</option>
<option>Samuel Wiqvist</option>
<option>Sebastian M. Schmon</option>
<option>Sebastian Wirkert</option>
<option>Sergei Seleznev</option>
<option>Song Liu</option>
<option>Stefan Engblom</option>
<option>Stefan T. Radev</option>
<option>Stephen Mrenna</option>
<option>Stephen R. Green</option>
<option>Theofanis Karaletsos</option>
<option>Thierry Mora</option>
<option>Thomas Moreau</option>
<option>Tomas Geffner</option>
<option>Tony Chen</option>
<option>Ulf K. Mertens</option>
<option>Ullrich Köthe</option>
<option>Umberto Picchini</option>
<option>Valentin De Bortoli</option>
<option>Veronika Rockova</option>
<option>Volodimir Begy</option>
<option>Yuexi Wang</option>
<option>Yuyang Shi</option>
<option>Álvaro Tejero-Cantero</option>
</select>
</span>
<div class="selector">
<span>Year: </span>
<select id="yearselection" class="selectpicker" multiple data-live-search="true">
<option>2016</option>
<option>2017</option>
<option>2018</option>
<option>2019</option>
<option>2020</option>
<option>2021</option>
<option>2022</option>
</select>
</div>
<span class="selector">
<span>Target: </span>
<select id="targetselection" class="selectpicker" multiple data-live-search="true">
<option>posterior</option>
<option>likelihood</option>
<option>likelihood-ratio</option>
<option>model-evidence</option>
<option>score</option>
</select>
</span>
<span class="selector">
<span>Neural Network: </span>
<select id="nnselection" class="selectpicker" multiple data-live-search="true">
<option>mixture-density</option>
<option>normalizing-flows</option>
<option>deep-ensembles</option>
<option>classifier-NN</option>
<option>any-NN</option>
<option>generative-NN</option>
<option>Bayesian-NN</option>
<option>score-network</option>
<option>autoencoders</option>
<option>??</option>
</select>
</span>
<span class="selector">
<span>Samples: </span>
<select id="samplesselection" class="selectpicker" multiple data-live-search="true">
<option>direct</option>
<option>MCMC</option>
<option>Stein Variational Gradient Descent</option>
<option>-</option>
<option>??</option>
<option>rejection</option>
<option>importance</option>
</select>
</span>
<span class="selector">
<span>Sequential: </span>
<select id="seqamselection" class="selectpicker" multiple data-live-search="true">
<option>yes</option>
<option>no</option>
<option>??</option>
</select>
</span>
</div>
{% assign id = 0 %}
<hr/>
<!--Post--------------------------------------------------------------------------------------------------------------------------------------------->
{% assign id = id | plus:1 %}
<div class="entry">
<div class="row">
<div class="column1" >
{% capture x %}
## Fast epsilon-free Inference of Simulation Models with Bayesian Conditional Density Estimation
{% endcapture %}{{ x | markdownify }}
</div>
<div class="column2" >
<div id="block_container">
<div id="bloc2">
<a href="https://arxiv.org/abs/1605.06376" target="_blank" class="btn btn-info" role="button"> Paper </a>
</div>
<div id="bloc3">
<p>
<button class="btn btn-primary" type="button" data-toggle="collapse" data-target="#collapse{{ id }}" aria-expanded="false" aria-controls="collapse{{ id }}">
See More
</button>
</p>
</div>
</div>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="dates"> May 20, 2016 </span>
<span class="authors"> George Papamakarios, Iain Murray </span>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="label_first"> Target: </span>
<span class="target"> posterior </span>
<span class="label"> Neural Network: </span> <span class="nn"> mixture-density </span>
<span class="label"> Samples: </span> <span class="samples"> direct </span>
<span class="label"> Sequential: </span> <span class="seq_am"> yes </span>
</div>
</div>
<div class="collapse" id="collapse{{ id }}">
{% capture x %}
* [Code](https://github.com/gpapamak/epsilon_free_inference)
{% endcapture %}{{ x | markdownify }}
</div>
<hr/>
</div>
<!--EndPost--------------------------------------------------------------------------------------------------------------------------------------------->
<!--Post--------------------------------------------------------------------------------------------------------------------------------------------->
{% assign id = id | plus:1 %}
<div class="entry">
<div class="row">
<div class="column1" >
{% capture x %}
## Flexible statistical inference for mechanistic models of neural dynamics
{% endcapture %}{{ x | markdownify }}
</div>
<div class="column2" >
<div id="block_container">
<div id="bloc2">
<a href="https://arxiv.org/abs/1711.01861" target="_blank" class="btn btn-info" role="button"> Paper </a>
</div>
<div id="bloc3">
<p>
<button class="btn btn-primary" type="button" data-toggle="collapse" data-target="#collapse{{ id }}" aria-expanded="false" aria-controls="collapse{{ id }}">
See More
</button>
</p>
</div>
</div>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="dates"> November 6, 2017 </span>
<span class="authors"> Jan-Matthis Lueckmann, Pedro J. Gonçalves, Giacomo Bassetto, Kaan Öcal, Marcel Nonnenmacher, Jakob H. Macke </span>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="label_first"> Target: </span>
<span class="target"> posterior </span>
<span class="label"> Neural Network: </span> <span class="nn"> normalizing-flows </span>
<span class="label"> Samples: </span> <span class="samples"> direct </span>
<span class="label"> Sequential: </span> <span class="seq_am"> yes </span>
</div>
</div>
<div class="collapse" id="collapse{{ id }}">
{% capture x %}
* [Code](https://github.com/mackelab/delfi)
{% endcapture %}{{ x | markdownify }}
</div>
<hr/>
</div>
<!--EndPost--------------------------------------------------------------------------------------------------------------------------------------------->
<!--Post--------------------------------------------------------------------------------------------------------------------------------------------->
{% assign id = id | plus:1 %}
<div class="entry">
<div class="row">
<div class="column1" >
{% capture x %}
## Hierarchical Implicit Models and Likelihood-Free Variational Inference
{% endcapture %}{{ x | markdownify }}
</div>
<div class="column2" >
<div id="block_container">
<div id="bloc2">
<a href="https://arxiv.org/abs/1702.08896" target="_blank" class="btn btn-info" role="button"> Paper </a>
</div>
<div id="bloc3">
<p>
<button class="btn btn-primary" type="button" data-toggle="collapse" data-target="#collapse{{ id }}" aria-expanded="false" aria-controls="collapse{{ id }}">
See More
</button>
</p>
</div>
</div>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="dates"> November, 2017 </span>
<span class="authors"> Dustin Tran, Rajesh Ranganath, David M. Blei </span>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="label_first"> Target: </span>
<span class="target"> posterior </span>
<span class="label"> Neural Network: </span> <span class="nn"> generative-NN </span>
<span class="label"> Samples: </span> <span class="samples"> direct </span>
<span class="label"> Sequential: </span> <span class="seq_am"> no </span>
</div>
</div>
<div class="collapse" id="collapse{{ id }}">
{% capture x %}
* Trains a generative network for inferring the posterior in hierarchical models.
* For non-hierarchical models, the approach is very similar to GATSBI (Ramesh et al., 2021)
{% endcapture %}{{ x | markdownify }}
</div>
<hr/>
</div>
<!--EndPost--------------------------------------------------------------------------------------------------------------------------------------------->
<!--Post--------------------------------------------------------------------------------------------------------------------------------------------->
{% assign id = id | plus:1 %}
<div class="entry">
<div class="row">
<div class="column1" >
{% capture x %}
## Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows
{% endcapture %}{{ x | markdownify }}
</div>
<div class="column2" >
<div id="block_container">
<div id="bloc2">
<a href="https://arxiv.org/abs/1805.07226" target="_blank" class="btn btn-info" role="button"> Paper </a>
</div>
<div id="bloc3">
<p>
<button class="btn btn-primary" type="button" data-toggle="collapse" data-target="#collapse{{ id }}" aria-expanded="false" aria-controls="collapse{{ id }}">
See More
</button>
</p>
</div>
</div>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="dates"> May 18, 2018 </span>
<span class="authors"> George Papamakarios, David C. Sterratt, Iain Murray </span>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="label_first"> Target: </span>
<span class="target"> likelihood </span>
<span class="label"> Neural Network: </span> <span class="nn"> normalizing-flows </span>
<span class="label"> Samples: </span> <span class="samples"> MCMC </span>
<span class="label"> Sequential: </span> <span class="seq_am"> yes </span>
</div>
</div>
<div class="collapse" id="collapse{{ id }}">
{% capture x %}
* [Code](https://github.com/gpapamak/snl)
{% endcapture %}{{ x | markdownify }}
</div>
<hr/>
</div>
<!--EndPost--------------------------------------------------------------------------------------------------------------------------------------------->
<!--Post--------------------------------------------------------------------------------------------------------------------------------------------->
{% assign id = id | plus:1 %}
<div class="entry">
<div class="row">
<div class="column1" >
{% capture x %}
## Likelihood-free inference with emulator networks
{% endcapture %}{{ x | markdownify }}
</div>
<div class="column2" >
<div id="block_container">
<div id="bloc2">
<a href="https://arxiv.org/abs/1805.09294" target="_blank" class="btn btn-info" role="button"> Paper </a>
</div>
<div id="bloc3">
<p>
</p>
</div>
</div>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="dates"> May 23, 2018 </span>
<span class="authors"> Jan-Matthis Lueckmann, Giacomo Bassetto, Theofanis Karaletsos, Jakob H. Macke </span>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="label_first"> Target: </span>
<span class="target"> likelihood </span>
<span class="label"> Neural Network: </span> <span class="nn"> deep-ensembles </span>
<span class="label"> Samples: </span> <span class="samples"> MCMC </span>
<span class="label"> Sequential: </span> <span class="seq_am"> yes </span>
</div>
</div>
<hr/>
</div>
<!--EndPost--------------------------------------------------------------------------------------------------------------------------------------------->
<!--Post--------------------------------------------------------------------------------------------------------------------------------------------->
{% assign id = id | plus:1 %}
<div class="entry">
<div class="row">
<div class="column1" >
{% capture x %}
## Analyzing Inverse Problems with Invertible Neural Networks
{% endcapture %}{{ x | markdownify }}
</div>
<div class="column2" >
<div id="block_container">
<div id="bloc2">
<a href="https://arxiv.org/abs/1808.04730" target="_blank" class="btn btn-info" role="button"> Paper </a>
</div>
<div id="bloc3">
<p>
<button class="btn btn-primary" type="button" data-toggle="collapse" data-target="#collapse{{ id }}" aria-expanded="false" aria-controls="collapse{{ id }}">
See More
</button>
</p>
</div>
</div>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="dates"> February, 2019 </span>
<span class="authors"> Lynton Ardizzone, Jakob Kruse, Sebastian Wirkert, Daniel Rahner, Eric W. Pellegrini, Ralf S. Klessen, Lena Maier-Hein, Carsten Rother, Ullrich Köthe </span>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="label_first"> Target: </span>
<span class="target"> posterior </span>
<span class="label"> Neural Network: </span> <span class="nn"> normalizing-flows </span>
<span class="label"> Samples: </span> <span class="samples"> direct </span>
<span class="label"> Sequential: </span> <span class="seq_am"> no </span>
</div>
</div>
<div class="collapse" id="collapse{{ id }}">
{% capture x %}
* Different way to train a normalizing flow for amortized posterior inference with respect to Radev et al. 2021.
{% endcapture %}{{ x | markdownify }}
</div>
<hr/>
</div>
<!--EndPost--------------------------------------------------------------------------------------------------------------------------------------------->
<!--Post--------------------------------------------------------------------------------------------------------------------------------------------->
{% assign id = id | plus:1 %}
<div class="entry">
<div class="row">
<div class="column1" >
{% capture x %}
## Likelihood-free MCMC with Amortized Approximate Ratio Estimators
{% endcapture %}{{ x | markdownify }}
</div>
<div class="column2" >
<div id="block_container">
<div id="bloc2">
<a href="https://arxiv.org/abs/1903.04057" target="_blank" class="btn btn-info" role="button"> Paper </a>
</div>
<div id="bloc3">
<p>
<button class="btn btn-primary" type="button" data-toggle="collapse" data-target="#collapse{{ id }}" aria-expanded="false" aria-controls="collapse{{ id }}">
See More
</button>
</p>
</div>
</div>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="dates"> March 10, 2019 </span>
<span class="authors"> Joeri Hermans, Volodimir Begy, Gilles Louppe </span>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="label_first"> Target: </span>
<span class="target"> likelihood-ratio </span>
<span class="label"> Neural Network: </span> <span class="nn"> classifier-NN </span>
<span class="label"> Samples: </span> <span class="samples"> MCMC </span>
<span class="label"> Sequential: </span> <span class="seq_am"> no </span>
</div>
</div>
<div class="collapse" id="collapse{{ id }}">
{% capture x %}
* [Code](https://github.com/montefiore-ai/hypothesis)
{% endcapture %}{{ x | markdownify }}
</div>
<hr/>
</div>
<!--EndPost--------------------------------------------------------------------------------------------------------------------------------------------->
<!--Post--------------------------------------------------------------------------------------------------------------------------------------------->
{% assign id = id | plus:1 %}
<div class="entry">
<div class="row">
<div class="column1" >
{% capture x %}
## Automatic posterior transformation for likelihood-free inference
{% endcapture %}{{ x | markdownify }}
</div>
<div class="column2" >
<div id="block_container">
<div id="bloc2">
<a href="https://arxiv.org/abs/1905.07488" target="_blank" class="btn btn-info" role="button"> Paper </a>
</div>
<div id="bloc3">
<p>
<button class="btn btn-primary" type="button" data-toggle="collapse" data-target="#collapse{{ id }}" aria-expanded="false" aria-controls="collapse{{ id }}">
See More
</button>
</p>
</div>
</div>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="dates"> May 17, 2019 </span>
<span class="authors"> David S. Greenberg, Marcel Nonnenmacher, Jakob H. Macke </span>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="label_first"> Target: </span>
<span class="target"> posterior </span>
<span class="label"> Neural Network: </span> <span class="nn"> normalizing-flows </span>
<span class="label"> Samples: </span> <span class="samples"> direct </span>
<span class="label"> Sequential: </span> <span class="seq_am"> yes </span>
</div>
</div>
<div class="collapse" id="collapse{{ id }}">
{% capture x %}
* [Code](https://github.com/mackelab/delfi)
{% endcapture %}{{ x | markdownify }}
</div>
<hr/>
</div>
<!--EndPost--------------------------------------------------------------------------------------------------------------------------------------------->
<!--Post--------------------------------------------------------------------------------------------------------------------------------------------->
{% assign id = id | plus:1 %}
<div class="entry">
<div class="row">
<div class="column1" >
{% capture x %}
## Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems
{% endcapture %}{{ x | markdownify }}
</div>
<div class="column2" >
<div id="block_container">
<div id="bloc2">
<a href="https://arxiv.org/abs/1905.12090" target="_blank" class="btn btn-info" role="button"> Paper </a>
</div>
<div id="bloc3">
<p>
<button class="btn btn-primary" type="button" data-toggle="collapse" data-target="#collapse{{ id }}" aria-expanded="false" aria-controls="collapse{{ id }}">
See More
</button>
</p>
</div>
</div>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="dates"> October, 2019 </span>
<span class="authors"> Geoffrey Roeder, Paul K. Grant, Andrew Phillips, Neil Dalchau, Edward Meeds </span>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="label_first"> Target: </span>
<span class="target"> posterior </span>
<span class="label"> Neural Network: </span> <span class="nn"> autoencoders </span>
<span class="label"> Samples: </span> <span class="samples"> direct </span>
<span class="label"> Sequential: </span> <span class="seq_am"> no </span>
</div>
</div>
<div class="collapse" id="collapse{{ id }}">
{% capture x %}
* [Code](https://github.com/Microsoft/vi-hds)
* Use variational inference to train autoencoders for posterior inference.
{% endcapture %}{{ x | markdownify }}
</div>
<hr/>
</div>
<!--EndPost--------------------------------------------------------------------------------------------------------------------------------------------->
<!--Post--------------------------------------------------------------------------------------------------------------------------------------------->
{% assign id = id | plus:1 %}
<div class="entry">
<div class="row">
<div class="column1" >
{% capture x %}
## The frontier of simulation-based inference
{% endcapture %}{{ x | markdownify }}
</div>
<div class="column2" >
<div id="block_container">
<div id="bloc2">
<a href="https://arxiv.org/abs/1911.01429" target="_blank" class="btn btn-info" role="button"> Paper </a>
</div>
<div id="bloc3">
<p>
<button class="btn btn-primary" type="button" data-toggle="collapse" data-target="#collapse{{ id }}" aria-expanded="false" aria-controls="collapse{{ id }}">
See More
</button>
</p>
</div>
</div>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="dates"> November 4, 2019 </span>
<span class="authors"> Kyle Cranmer, Johann Brehmer, Gilles Louppe </span>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="target"> </span>
<span class="nn"> </span>
<span class="samples"> </span>
<span class="seq_am"> </span>
<span class="additional_description"> Review paper </span>
</div>
</div>
<div class="collapse" id="collapse{{ id }}">
{% capture x %}
* They discuss recent advaces in LFI, including NN-based methods.
{% endcapture %}{{ x | markdownify }}
</div>
<hr/>
</div>
<!--EndPost--------------------------------------------------------------------------------------------------------------------------------------------->
<!--Post--------------------------------------------------------------------------------------------------------------------------------------------->
{% assign id = id | plus:1 %}
<div class="entry">
<div class="row">
<div class="column1" >
{% capture x %}
## On Contrastive Learning for Likelihood-free Inference
{% endcapture %}{{ x | markdownify }}
</div>
<div class="column2" >
<div id="block_container">
<div id="bloc2">
<a href="https://arxiv.org/abs/2002.03712" target="_blank" class="btn btn-info" role="button"> Paper </a>
</div>
<div id="bloc3">
<p>
<button class="btn btn-primary" type="button" data-toggle="collapse" data-target="#collapse{{ id }}" aria-expanded="false" aria-controls="collapse{{ id }}">
See More
</button>
</p>
</div>
</div>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="dates"> February 10, 2020 </span>
<span class="authors"> Conor Durkan, Iain Murray, George Papamakarios </span>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="label_first"> Target: </span>
<span class="target"> likelihood-ratio, posterior </span>
<span class="label"> Neural Network: </span> <span class="nn"> classifier-NN, normalizing-flows </span>
<span class="label"> Samples: </span> <span class="samples"> MCMC, direct </span>
<span class="label"> Sequential: </span> <span class="seq_am"> yes </span>
</div>
</div>
<div class="collapse" id="collapse{{ id }}">
{% capture x %}
* [Code](https://github.com/conormdurkan/lfi)
* They unify the ratio estimation approach by Hermans et al. (2019) with the sequential neural posterior formulation of Greenberg et al. (2019).
{% endcapture %}{{ x | markdownify }}
</div>
<hr/>
</div>
<!--EndPost--------------------------------------------------------------------------------------------------------------------------------------------->
<!--Post--------------------------------------------------------------------------------------------------------------------------------------------->
{% assign id = id | plus:1 %}
<div class="entry">
<div class="row">
<div class="column1" >
{% capture x %}
## BayesFlow: Learning complex stochastic models with invertible neural networks
{% endcapture %}{{ x | markdownify }}
</div>
<div class="column2" >
<div id="block_container">
<div id="bloc2">
<a href="https://arxiv.org/abs/2003.06281" target="_blank" class="btn btn-info" role="button"> Paper </a>
</div>
<div id="bloc3">
<p>
<button class="btn btn-primary" type="button" data-toggle="collapse" data-target="#collapse{{ id }}" aria-expanded="false" aria-controls="collapse{{ id }}">
See More
</button>
</p>
</div>
</div>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="dates"> March 13, 2020 </span>
<span class="authors"> Stefan T. Radev, Ulf K. Mertens, Andreas Voss, Lynton Ardizzone, Ullrich Köthe </span>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="label_first"> Target: </span>
<span class="target"> posterior </span>
<span class="label"> Neural Network: </span> <span class="nn"> normalizing-flows </span>
<span class="label"> Samples: </span> <span class="samples"> direct </span>
<span class="label"> Sequential: </span> <span class="seq_am"> no </span>
</div>
</div>
<div class="collapse" id="collapse{{ id }}">
{% capture x %}
* [Code](https://github.com/stefanradev93/cINN)
{% endcapture %}{{ x | markdownify }}
</div>
<hr/>
</div>
<!--EndPost--------------------------------------------------------------------------------------------------------------------------------------------->
<!--Post--------------------------------------------------------------------------------------------------------------------------------------------->
{% assign id = id | plus:1 %}
<div class="entry">
<div class="row">
<div class="column1" >
{% capture x %}
## Distortion estimates for approximate Bayesian inference
{% endcapture %}{{ x | markdownify }}
</div>
<div class="column2" >
<div id="block_container">
<div id="bloc2">
<a href="https://arxiv.org/abs/2006.11228" target="_blank" class="btn btn-info" role="button"> Paper </a>
</div>
<div id="bloc3">
<p>
<button class="btn btn-primary" type="button" data-toggle="collapse" data-target="#collapse{{ id }}" aria-expanded="false" aria-controls="collapse{{ id }}">
See More
</button>
</p>
</div>
</div>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="dates"> June 19, 2020 </span>
<span class="authors"> Hanwen Xing, Geoff Nicholls, Jeong (Kate) Lee </span>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="label_first"> Target: </span>
<span class="target"> posterior </span>
<span class="label"> Neural Network: </span> <span class="nn"> normalizing-flows </span>
<span class="label"> Samples: </span> <span class="samples"> direct </span>
<span class="label"> Sequential: </span> <span class="seq_am"> no </span>
</div>
</div>
<div class="collapse" id="collapse{{ id }}">
{% capture x %}
* They use normalizing flows to build distortion maps which acts on univariate marginals of the approximate posterior to move them closer to the exact posterior, without evaluation of the latter, for a specific observation.
* Can be applied to any posterior approximation, provided simulations from the model are doable.
{% endcapture %}{{ x | markdownify }}
</div>
<hr/>
</div>
<!--EndPost--------------------------------------------------------------------------------------------------------------------------------------------->
<!--Post--------------------------------------------------------------------------------------------------------------------------------------------->
{% assign id = id | plus:1 %}
<div class="entry">
<div class="row">
<div class="column1" >
{% capture x %}
## SBI -- A toolkit for simulation-based inference
{% endcapture %}{{ x | markdownify }}
</div>
<div class="column2" >
<div id="block_container">
<div id="bloc2">
<a href="https://arxiv.org/abs/2007.09114" target="_blank" class="btn btn-info" role="button"> Paper </a>
</div>
<div id="bloc3">
<p>
<button class="btn btn-primary" type="button" data-toggle="collapse" data-target="#collapse{{ id }}" aria-expanded="false" aria-controls="collapse{{ id }}">
See More
</button>
</p>
</div>
</div>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="dates"> July 17, 2020 </span>
<span class="authors"> Alvaro Tejero-Cantero, Jan Boelts, Michael Deistler, Jan-Matthis Lueckmann, Conor Durkan, Pedro J. Gonçalves, David S. Greenberg, Jakob H. Macke </span>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="target"> </span>
<span class="nn"> </span>
<span class="samples"> </span>
<span class="seq_am"> </span>
<span class="additional_description"> Python package </span>
</div>
</div>
<div class="collapse" id="collapse{{ id }}">
{% capture x %}
* [Code](https://github.com/mackelab/sbi)
* A Python toolbox for simulation-based inference methods based on NNs.
* Documentation at [this webpage](https://www.mackelab.org/sbi/)
{% endcapture %}{{ x | markdownify }}
</div>
<hr/>
</div>
<!--EndPost--------------------------------------------------------------------------------------------------------------------------------------------->
<!--Post--------------------------------------------------------------------------------------------------------------------------------------------->
{% assign id = id | plus:1 %}
<div class="entry">
<div class="row">
<div class="column1" >
{% capture x %}
## Error-Guided Likelihood-Free MCMC
{% endcapture %}{{ x | markdownify }}
</div>
<div class="column2" >
<div id="block_container">
<div id="bloc2">
<a href="https://ieeexplore.ieee.org/abstract/document/9533611" target="_blank" class="btn btn-info" role="button"> Paper </a>
</div>
<div id="bloc3">
<p>
<button class="btn btn-primary" type="button" data-toggle="collapse" data-target="#collapse{{ id }}" aria-expanded="false" aria-controls="collapse{{ id }}">
See More
</button>
</p>
</div>
</div>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="dates"> October 13, 2020 </span>
<span class="authors"> Volodimir Begy, Erich Schikuta </span>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="label_first"> Target: </span>
<span class="target"> likelihood-ratio </span>
<span class="label"> Neural Network: </span> <span class="nn"> classifier-NN </span>
<span class="label"> Samples: </span> <span class="samples"> MCMC </span>
<span class="label"> Sequential: </span> <span class="seq_am"> no </span>
</div>
</div>
<div class="collapse" id="collapse{{ id }}">
{% capture x %}
* Enables parameter inference for observations that are a given distance away from the true observation
* Use distance of 0 for vanilla inference
* Handy in applications with high-dimensional observations
{% endcapture %}{{ x | markdownify }}
</div>
<hr/>
</div>
<!--EndPost--------------------------------------------------------------------------------------------------------------------------------------------->
<!--Post--------------------------------------------------------------------------------------------------------------------------------------------->
{% assign id = id | plus:1 %}
<div class="entry">
<div class="row">
<div class="column1" >
{% capture x %}
## Simulation-efficient marginal posterior estimation with swyft: stop wasting your precious time
{% endcapture %}{{ x | markdownify }}
</div>
<div class="column2" >
<div id="block_container">
<div id="bloc2">
<a href="https://arxiv.org/abs/2011.13951" target="_blank" class="btn btn-info" role="button"> Paper </a>
</div>
<div id="bloc3">
<p>
<button class="btn btn-primary" type="button" data-toggle="collapse" data-target="#collapse{{ id }}" aria-expanded="false" aria-controls="collapse{{ id }}">
See More
</button>
</p>
</div>
</div>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="dates"> November 27, 2020 </span>
<span class="authors"> Benjamin Kurt Miller, Alex Cole, Gilles Louppe, Christoph Weniger </span>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="label_first"> Target: </span>
<span class="target"> likelihood-ratio </span>
<span class="label"> Neural Network: </span> <span class="nn"> any-NN </span>
<span class="label"> Samples: </span> <span class="samples"> MCMC, rejection, importance </span>
<span class="label"> Sequential: </span> <span class="seq_am"> yes </span>
</div>
</div>
<div class="collapse" id="collapse{{ id }}">
{% capture x %}
* Explored truncation and estimating marginals as an options to increase simulation-effiency
* Expanded upon in Truncated Marginal Neural Ratio Estimation
* This version of swyft has the code [code](https://github.com/undark-lab/swyft/releases/tag/v0.1.2)
{% endcapture %}{{ x | markdownify }}
</div>
<hr/>
</div>
<!--EndPost--------------------------------------------------------------------------------------------------------------------------------------------->
<!--Post--------------------------------------------------------------------------------------------------------------------------------------------->
{% assign id = id | plus:1 %}
<div class="entry">
<div class="row">
<div class="column1" >
{% capture x %}
## Likelihood approximation networks (LANs) for fast inference of simulation models in cognitive neuroscience
{% endcapture %}{{ x | markdownify }}
</div>
<div class="column2" >
<div id="block_container">
<div id="bloc2">
<a href="https://elifesciences.org/articles/65074#fig1" target="_blank" class="btn btn-info" role="button"> Paper </a>
</div>
<div id="bloc3">
<p>
<button class="btn btn-primary" type="button" data-toggle="collapse" data-target="#collapse{{ id }}" aria-expanded="false" aria-controls="collapse{{ id }}">
See More
</button>
</p>
</div>
</div>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="dates"> December 2, 2020 </span>
<span class="authors"> Alexander Fengler, Lakshmi N Govindarajan, Tony Chen, Michael J Frank </span>
</div>
</div>
<div class="row">
<div id="bloc2">
<span class="label_first"> Target: </span>
<span class="target"> likelihood </span>
<span class="label"> Neural Network: </span> <span class="nn"> any-NN </span>
<span class="label"> Samples: </span> <span class="samples"> MCMC </span>
<span class="label"> Sequential: </span> <span class="seq_am"> no </span>
</div>
</div>
<div class="collapse" id="collapse{{ id }}">
{% capture x %}
* [Code](https://github.com/lnccbrown/lans)
* They use NNs to directly approximate likelihood estimates obtained by KDE or binned empirical estimates from repeated model simulations.
{% endcapture %}{{ x | markdownify }}
</div>
<hr/>
</div>
<!--EndPost--------------------------------------------------------------------------------------------------------------------------------------------->
<!--Post--------------------------------------------------------------------------------------------------------------------------------------------->
{% assign id = id | plus:1 %}
<div class="entry">
<div class="row">
<div class="column1" >
{% capture x %}
## Score Matched Neural Exponential Families for Likelihood-Free Inference
{% endcapture %}{{ x | markdownify }}
</div>
<div class="column2" >
<div id="block_container">
<div id="bloc2">
<a href="https://arxiv.org/abs/2012.10903" target="_blank" class="btn btn-info" role="button"> Paper </a>
</div>
<div id="bloc3">