https://computo-journal.org/ Latest published articles from Computo Journal http://computo-journal.org/published-202301-chagneux-macrolitter/ http://computo-journal.org/published-202301-chagneux-macrolitter/ Thu, 16 Feb 2023 00:00:00 GMT Litter is a known cause of degradation in marine environments and most of it travels in rivers before reaching the oceans. In this paper, we present a novel algorithm to assist waste monitoring along watercourses. While several attempts have been made to quantify litter using neural object detection in photographs of floating items, we tackle the more challenging task of counting directly in videos using boat-embedded cameras. We rely on multi-object tracking (MOT) but focus on the key pitfalls of false and redundant counts which arise in typical scenarios of poor detection performance. Our system only requires supervision at the image level and performs Bayesian filtering via a state space model based on optical flow. We present a new open image dataset gathered through a crowdsourced campaign and used to train a center-based anchor-free object detector. Realistic video footage assembled by water monitoring experts is annotated and provided for evaluation. Improvements in count quality are demonstrated against systems built from state-of-the-art multi-object trackers sharing the same detection capabilities. A precise error decomposition allows clear analysis and highlights the remaining challenges. http://computo-journal.org/published-202301-boulin-clayton/ http://computo-journal.org/published-202301-boulin-clayton/ Thu, 12 Jan 2023 00:00:00 GMT The package \$\textbackslash textsf\{clayton\}\$ is designed to be intuitive, user-friendly, and efficient. It offers a wide range of copula models, including Archimedean, Elliptical, and Extreme. The package is implemented in pure \$\textbackslash textsf\{Python\}\$, making it easy to install and use. In addition, we provide detailed documentation and examples to help users get started quickly. We also conduct a performance comparison with existing \$\textbackslash textsf\{R\}\$ packages, demonstrating the efficiency of our implementation. The \$\textbackslash textsf\{clayton\}\$ package is a valuable tool for researchers and practitioners working with copulae in \$\textbackslash textsf\{Python\}\$. https://computo-journal.org/published-202204-deeplearning-occupancy-lynx/ https://computo-journal.org/published-202204-deeplearning-occupancy-lynx/ Fri, 22 Apr 2022 00:00:00 GMT Deep learning is used in computer vision problems with important applications in several scientific fields. In ecology for example, there is a growing interest in deep learning for automatizing repetitive analyses on large amounts of images, such as animal species identification. However, there are challenging issues toward the wide adoption of deep learning by the community of ecologists. First, there is a programming barrier as most algorithms are written in `Python` while most ecologists are versed in `R`. Second, recent applications of deep learning in ecology have focused on computational aspects and simple tasks without addressing the underlying ecological questions or carrying out the statistical data analysis to answer these questions. Here, we showcase a reproducible `R` workflow integrating both deep learning and statistical models using predator-prey relationships as a case study. We illustrate deep learning for the identification of animal species on images collected with camera traps, and quantify spatial co-occurrence using multispecies occupancy models. Despite average model classification performances, ecological inference was similar whether we analysed the ground truth dataset or the classified dataset. This result calls for further work on the trade-offs between time and resources allocated to train models with deep learning and our ability to properly address key ecological questions with biodiversity monitoring. We hope that our reproducible workflow will be useful to ecologists and applied statisticians. http://computo-journal.org/published-202312-cleynen-local/ http://computo-journal.org/published-202312-cleynen-local/ Thu, 14 Dec 2023 00:00:00 GMT Random Forests (RF) {[}@breiman:2001{]} are very popular machine learning methods. They perform well even with little or no tuning, and have some theoretical guarantees, especially for sparse problems {[}@biau:2012;@scornet:etal:2015{]}. These learning strategies have been used in several contexts, also outside the field of classification and regression. To perform Bayesian model selection in the case of intractable likelihoods, the ABC Random Forests (ABC-RF) strategy of @pudlo:etal:2016 consists in applying Random Forests on training sets composed of simulations coming from the Bayesian generative models. The ABC-RF technique is based on an underlying RF for which the training and prediction phases are separated. The training phase does not take into account the data to be predicted. This seems to be suboptimal as in the ABC framework only one observation is of interest for the prediction. In this paper, we study tree-based methods that are built to predict a specific instance in a classification setting. This type of methods falls within the scope of local (lazy/instance-based/case specific) classification learning. We review some existing strategies and propose two new ones. The first consists in modifying the tree splitting rule by using kernels, the second in using a first RF to compute some local variable importance that is used to train a second, more local, RF. Unfortunately, these approaches, although interesting, do not provide conclusive results. http://computo-journal.org/published-202412-ambroise-spectral/ http://computo-journal.org/published-202412-ambroise-spectral/ Fri, 13 Dec 2024 00:00:00 GMT In this paper, Spectral Bridges, a novel clustering algorithm, is introduced. This algorithm builds upon the traditional k-means and spectral clustering frameworks by subdividing data into small Voronoï regions, which are subsequently merged according to a connectivity measure. Drawing inspiration from Support Vector Machine’s margin concept, a non-parametric clustering approach is proposed, building an affinity margin between each pair of Voronoï regions. This approach delineates intricate, non-convex cluster structures and is robust to hyperparameter choice. The numerical experiments underscore Spectral Bridges as a fast, robust, and versatile tool for clustering tasks spanning diverse domains. Its efficacy extends to large-scale scenarios encompassing both real-world and synthetic datasets. The Spectral Bridge algorithm is implemented both in Python (\textless https://pypi.org/project/spectral-bridges\textgreater) and R \textless https://github.com/cambroise/spectral-bridges-Rpackage\textgreater). https://computo-journal.org/published-202512-baillie-varp/ https://computo-journal.org/published-202512-baillie-varp/ Mon, 01 Dec 2025 00:00:00 GMT 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. http://computo-journal.org/published-202311-delattre-fim/ http://computo-journal.org/published-202311-delattre-fim/ Tue, 21 Nov 2023 00:00:00 GMT The Fisher information matrix (FIM) is a key quantity in statistics. However its exact computation is often not trivial. In particular in many latent variable models, it is intricated due to the presence of unobserved variables. Several methods have been proposed to approximate the FIM when it can not be evaluated analytically. Different estimates have been considered, in particular moment estimates. However some of them require to compute second derivatives of the complete data log-likelihood which leads to some disadvantages. In this paper, we focus on the empirical Fisher information matrix defined as an empirical estimate of the covariance matrix of the score, which only requires to compute the first derivatives of the log-likelihood. Our contribution consists in presenting a new numerical method to evaluate this empirical Fisher information matrix in latent variable model when the proposed estimate can not be directly analytically evaluated. We propose a stochastic approximation estimation algorithm to compute this estimate as a by-product of the parameter estimate. We evaluate the finite sample size properties of the proposed estimate and the convergence properties of the estimation algorithm through simulation studies. https://computo-journal.org/published-202511-alquier-regmmd/ https://computo-journal.org/published-202511-alquier-regmmd/ Tue, 18 Nov 2025 00:00:00 GMT The Maximum Mean Discrepancy (MMD) is a kernel-based metric widely used for nonparametric tests and estimation. Recently, it has also been studied as an objective function for parametric estimation, as it has been shown to yield robust estimators. We have implemented MMD minimization for parameter inference in a wide range of statistical models, including various regression models, within an `R` package called `regMMD`. This paper provides an introduction to the `regMMD` package. We describe the available kernels and optimization procedures, as well as the default settings. Detailed applications to simulated and real data are provided. https://computo-journal.org/published-202510-durand-fast/ https://computo-journal.org/published-202510-durand-fast/ Thu, 09 Oct 2025 00:00:00 GMT This paper presents a new algorithm (and an additional trick) that allows to compute fastly an entire curve of post hoc bounds for the False Discovery Proportion when the underlying bound \$V\^{}*\_\{\textbackslash mathfrak\{R\}\}\$ construction is based on a reference family \$\textbackslash mathfrak\{R\}\$ with a forest structure à la @MR4178188. By an entire curve, we mean the values \$V\^{}*\_\{\textbackslash mathfrak\{R\}\}(S\_1),\textbackslash dotsc,V\^{}*\_\{\textbackslash mathfrak\{R\}\}(S\_m)\$ computed on a path of increasing selection sets \$S\_1\textbackslash subsetneq\textbackslash dotsb\textbackslash subsetneq S\_m\$, \$\textbar S\_t\textbar=t\$. The new algorithm leverages the fact that going from \$S\_t\$ to \$S\_\{t+1\}\$ is done by adding only one hypothesis. Compared to a more naive approach, the new algorithm has a complexity in \$O(\textbar\textbackslash mathcal K\textbar m)\$ instead of \$O(\textbar\textbackslash mathcal K\textbar m\^{}2)\$, where \$\textbar\textbackslash mathcal K\textbar\$ is the cardinality of the family. https://computo-journal.org/published-202509-boulet-simulator/ https://computo-journal.org/published-202509-boulet-simulator/ Mon, 08 Sep 2025 00:00:00 GMT This study investigates the use of Variational Auto-Encoders to build a simulator that approximates the law of genuine observations. Using both simulated and real data in scenarios involving counterfactuality, we discuss the general task of evaluating a simulator’s quality, with a focus on comparisons of statistical properties and predictive performance. While the simulator built from simulated data shows minor discrepancies, the results with real data reveal more substantial challenges. Beyond the technical analysis, we reflect on the broader implications of simulator design, and consider its role in modeling reality.