Update publications from getcomputo-pub.fsx [skip ci]

Author: github-actions[bot]

site/published.yml

@@ -1,3 +1,73 @@
+- abstract': >-
+    In Bayesian statistics, the choice of the prior can have
+        an important influence on the posterior and the parameter
+        estimation, especially when few data samples are available. To limit
+        the added subjectivity from a priori information, one can use the
+        framework of objective priors, more particularly, we focus on
+        reference priors in this work. However, computing such priors is a
+        difficult task in general. Hence, we consider cases where the
+        reference prior simplifies to the Jeffreys prior. We develop in this
+        paper a flexible algorithm based on variational inference which
+        computes approximations of priors from a set of parametric
+        distributions using neural networks. We also show that our algorithm
+        can retrieve modified Jeffreys priors when constraints are specified
+        in the optimization problem to ensure the solution is proper. We
+        propose a simple method to recover a relevant approximation of the
+        parametric posterior distribution using Markov Chain Monte Carlo
+        (MCMC) methods even if the density function of the parametric prior
+        is not known in general. Numerical experiments on several
+        statistical models of increasing complexity are presented. We show
+        the usefulness of this approach by recovering the target
+        distribution. The performance of the algorithm is evaluated on both
+        prior and posterior distributions, jointly using variational
+        inference and MCMC sampling.
+  authors: Nils Baillie, Antoine Van Biesbroeck and Clément Gauchy
+  bibtex: >+
+    @article{baillie2025,
+    	author = {Baillie, Nils and Van Biesbroeck, Antoine and Gauchy,
+        Clément},
+    	publisher = {French Statistical Society},
+    	title = {Variational Inference for Approximate Objective Priors Using
+        Neural Networks},
+    	journal = {Computo},
+    	date = {2025-12-01},
+    	doi = {10.57750/76fh-t442},
+    	issn = {2824-7795},
+    	langid = {en},
+    	abstract = {In Bayesian statistics, the choice of the prior can have
+        an important influence on the posterior and the parameter
+        estimation, especially when few data samples are available. To limit
+        the added subjectivity from a priori information, one can use the
+        framework of objective priors, more particularly, we focus on
+        reference priors in this work. However, computing such priors is a
+        difficult task in general. Hence, we consider cases where the
+        reference prior simplifies to the Jeffreys prior. We develop in this
+        paper a flexible algorithm based on variational inference which
+        computes approximations of priors from a set of parametric
+        distributions using neural networks. We also show that our algorithm
+        can retrieve modified Jeffreys priors when constraints are specified
+        in the optimization problem to ensure the solution is proper. We
+        propose a simple method to recover a relevant approximation of the
+        parametric posterior distribution using Markov Chain Monte Carlo
+        (MCMC) methods even if the density function of the parametric prior
+        is not known in general. Numerical experiments on several
+        statistical models of increasing complexity are presented. We show
+        the usefulness of this approach by recovering the target
+        distribution. The performance of the algorithm is evaluated on both
+        prior and posterior distributions, jointly using variational
+        inference and MCMC sampling.}
+    }
+
+  date: 2025-12-01
+  description: ''
+  doi: 10.57750/76fh-t442
+  draft: false
+  journal: Computo
+  pdf: ''
+  repo: published-202512-baillie-varp
+  title: Variational inference for approximate objective priors using neural networks
+  url: ''
+  year: 2025
 - abstract': >-
     The Maximum Mean Discrepancy (MMD) is a kernel-based
         metric widely used for nonparametric tests and estimation. Recently,