Publish and perish
In construction
This is merely a draft of the page, obtained after extracting a bibtex file from Google Scholar.
Papers
Mengersen, K. L., Pudlo, P., & Robert, C. P. (n.d.). Approximate bayesian computation via empirical likelihood.
Sedki, M. A., Marin, J.-M., & Pudlo, P. (n.d.). Convergence de l’algorithme AMIS (adaptive multiple importance sampling). 44èmes Journées de Statistiques, SFDS, Bruxelles.
Bonafos, G., Freyermuth, J.-M., Pudlo, P., Tronçon, S., & Rey, A. (2023). Topological data analysis of human vowels: Persistent homologies across representation spaces. arXiv Preprint arXiv:2310.06508.
Bonafos, G., Pudlo, P., Freyermuth, J.-M., Legou, T., Fagot, J., Tronçon, S., & Rey, A. (2023). Detection and classification of vocal productions in large scale audio recordings. arXiv Preprint arXiv:2302.07640.
Bonafos, G., Pudlo, P., Freyermuth, J.-M., Legou, T., Tronçon, S., Rey, A., et al. (2023). Detecting human and non-human vocal productions in large scale audio recordings. Samuel and Rey, Arnaud, Detecting Human and Non-Human Vocal Productions in Large Scale Audio Recordings.
Shakil, A., Khalighi, M. A., Pudlo, P., Leclerc, C., Laplace, D., Hamon, F., & Boudonne, A. (2023). Outlier detection in non-stationary time series applied to sewer network monitoring. Internet of Things, 21, 100654.
Aufort, G., Pudlo, P., & Burgarella, D. (2022). Tempered, anti-trunctated, multiple importance sampling. arXiv Preprint arXiv:2205.01501.
Guillot, J., Dominici, C., Lucchesi, A., Nguyen, H. T. T., Puget, A., Hocine, M., Rangel-Sosa, M. M., Simic, M., Nigri, J., Guillaumond, F., et al. (2022). Sympathetic axonal sprouting induces changes in macrophage populations and protects against pancreatic cancer. Nature Communications, 13(1), 1985.
Aufort, G., Ciesla, L., Pudlo, P., & Buat, V. (2020). Constraining the recent star formation history of galaxies: An approximate bayesian computation approach. Astronomy & Astrophysics, 635, A136.
Mengersen, K. L., Pudlo, P., & Robert, C. P. (2020). Case studies in applied bayesian data science. Springer International Publishing.
Stoehr, J., Pudlo, P., & Friel, N. (2020). GiRaF: A toolbox for gibbs random fields analysis. R package version.
Ebert, A., Pudlo, P., Mengersen, K., Wu, P., & Drovandi, C. (2019). Combined parameter and state inference with automatically calibrated ABC. arXiv Preprint arXiv:1910.14227.
Marin, J.-M., Pudlo, P., & Sedki, M. (2019). Consistency of adaptive importance sampling and recycling schemes.
Raynal, L., Marin, J.-M., Pudlo, P., Ribatet, M., Robert, C. P., & Estoup, A. (2019). ABC random forests for bayesian parameter inference. Bioinformatics, 35(10), 1720–1728.
Estoup, A., Verdu, P., Marin, J.-M., Robert, C., Dehne-Garcia, A., Cornuet, J.-M., & Pudlo, P. (2018). Application of ABC to infer the genetic history of pygmy hunter-gatherer populations from western central africa. In Handbook of approximate bayesian computation (pp. 541–567). Chapman; Hall/CRC.
Grollemund, P.-M., Abraham, C., Baragatti, M., & Pudlo, P. (2018). Elicitation of experts’ knowledge for functional linear regression. 2018 ISBA World Meeting, 1–p.
Marin, J.-M., Pudlo, P., Estoup, A., & Robert, C. (2018). Likelihood-free model choice. In Handbook of approximate bayesian computation (pp. 153–178). Chapman; Hall/CRC.
Pudlo, P., & Sedki, M. (2018). Simulation of stochastic models of structured population in population genetics under neutrality. Journal de La Société Française de Statistique, 159(3), 126–141.
Fraimout, A., Debat, V., Fellous, S., Hufbauer, R. A., Foucaud, J., Pudlo, P., Marin, J.-M., Price, D. K., Cattel, J., Chen, X., et al. (2017). Deciphering the routes of invasion of drosophila suzukii by means of ABC random forest. Molecular Biology and Evolution, 34(4), 980–996.
Marin, J.-M., Pudlo, P., Raynal, L., Estoup, A. A., & Robert, C. P. (2017). Approximate bayesian computation using random forest. Validating and Expanding Approximate Bayesian Computation Methods (17w5025).
Merle, C., Leblois, R., Rousset, F., & Pudlo, P. (2017). Resampling: An improvement of importance sampling in varying population size models. Theoretical Population Biology, 114, 70–87.
Grollemund, P.-M., Abraham, C., Pudlo, P., & Baragatti, M. (2016). Bayesian linear functional regression with sparse step function. 22. International Conference on Computational Statistics (COMPSTAT). Satellite CRoNoS Workshop on Functional Data Analysis, 101–p.
Pudlo, P., Marin, J.-M., Estoup, A., Cornuet, J.-M., Gautier, M., & Robert, C. P. (2016). Reliable ABC model choice via random forests. Bioinformatics, 32(6), 859–866.
Cucala, L., Stoehr, J., & Pudlo, P. (2015). Adaptive ABC model choice and geometric summary statistics for hidden gibbs random fields. HAL, 2015.
Grollemund, P.-M., Abraham, C., Baragatti, M., & Pudlo, P. (2015). Interpretable bayesian functional linear regression. 47. Journées de Statistique de La SFdS, np.
Stoehr, J., Pudlo, P., & Cucala, L. (2015). Adaptive ABC model choice and geometric summary statistics for hidden gibbs random fields. Statistics and Computing, 25, 129–141.
Baragatti, M., & Pudlo, P. (2014). An overview on approximate bayesian computation. ESAIM: Proceedings, 44, 291–299.
Cornuet, J.-M., Pudlo, P., Veyssier, J., Dehne-Garcia, A., Gautier, M., Leblois, R., Marin, J.-M., & Estoup, A. (2014). DIYABC v2. 0: A software to make approximate bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data. Bioinformatics, 30(8), 1187–1189.
Leblois, R., Pudlo, P., Néron, J., Bertaux, F., Reddy Beeravolu, C., Vitalis, R., & Rousset, F. (2014). Maximum-likelihood inference of population size contractions from microsatellite data. Molecular Biology and Evolution, 31(10), 2805–2823.
Pudlo, P. (2014). CONTRIBUTIONS À LA STATISTIQUE COMPUTATIONNELLE ET À LA CLASSIFICATION NON SUPERVISÉe [PhD thesis]. Université Montpellier 2.
Cadre, B., Pelletier, B., & Pudlo, P. (2013). Estimation of density level sets with a given probability content. Journal of Nonparametric Statistics, 25(1), 261–272.
Gautier, M., Foucaud, J., Gharbi, K., Cézard, T., Galan, M., Loiseau, A., Thomson, M., Pudlo, P., Kerdelhué, C., & Estoup, A. (2013). Estimation of population allele frequencies from next-generation sequencing data: Pool-versus individual-based genotyping. Molecular Ecology, 22(14), 3766–3779.
Gautier, M., Gharbi, K., Cezard, T., Foucaud, J., Kerdelhué, C., Pudlo, P., Cornuet, J.-M., & Estoup, A. (2013). The effect of RAD allele dropout on the estimation of genetic variation within and between populations. Molecular Ecology, 22(11), 3165–3178.
Mengersen, K. L., Pudlo, P., & Robert, C. P. (2013). Bayesian computation via empirical likelihood. Proceedings of the National Academy of Sciences, 110(4), 1321–1326.
Arias-Castro, E., Pelletier, B., & Pudlo, P. (2012). The normalized graph cut and cheeger constant: From discrete to continuous. Advances in Applied Probability, 44(4), 907–937.
Estoup, A., Lombaert, E., Marin, J.-M., Guillemaud, T., Pudlo, P., Robert, C. P., & Cornuet, J.-M. (2012). Estimation of demo-genetic model probabilities with approximate bayesian computation using linear discriminant analysis on summary statistics. Molecular Ecology Resources, 12(5), 846–855.
Marin, J.-M., Pudlo, P., Robert, C. P., & Ryder, R. J. (2012). Approximate bayesian computational methods. Statistics and Computing, 22(6), 1167–1180.
Marin, J.-M., Pudlo, P., & Sedki, M. (2012). Consistency of adaptive importance sampling and recycling schemes. arXiv Preprint arXiv:1211.2548.
Marin, J.-M., Pudlo, P., & Sedki, M. (2012). Optimal parallelization of a sequential approximate bayesian computation algorithm. Proceedings of the 2012 Winter Simulation Conference (WSC), 1–7.
Sedki, M., & Pudlo, P. (2012). Contribution to the discussion of fearnhead and prangle (2012). Constructing summary statistics for approximate bayesian computation: Semi-automatic approximate bayesian computation. Journal of the Royal Statistical Society: Series B, 74, 466–467.
Sedki, M., Pudlo, P., Marin, J.-M., Robert, C. P., & Cornuet, J.-M. (2012). Efficient learning in ABC algorithms. arXiv Preprint arXiv:1210.1388.
Pelletier, B., & Pudlo, P. (2011). Operator norm convergence of spectral clustering on level sets. The Journal of Machine Learning Research, 12, 385–416.
Ratmann, O., Pudlo, P., Richardson, S., & Robert, C. (2011). Monte carlo algorithms for model assessment via conflicting summaries. arXiv Preprint arXiv:1106.5919.
Arias-Castro, E., Pelletier, B., & Pudlo, P. (2010). Convergence de la constante de cheeger de graphes de voisinage. 42èmes Journées de Statistique, SFdS, Marseille.
Pudlo, P. (2010). Large deviations and full edgeworth expansions for finite markov chains with applications to the analysis of genomic sequences. ESAIM: Probability and Statistics, 14, 435–455.
Pelletier, B., & Pudlo, P. (2009). Estimation du nombre de clusters à l’aide de l’algorithme de clustering spectral. 41èmes Journées de Statistique, SFdS, Bordeaux.
Pudlo, P. (2004). Estimations précises de grandes déviations et applications à la statistique des séquences biologiques [PhD thesis]. Université Claude Bernard-Lyon I.
Pudlo, P. (2004). Precise estimates of large deviations with applications to biological sequence analysis. HAL, 2004.