Research

Papers, code, supplementary material and erratum

Here you can find:

• a list of my research articles and papers,

• a list of links to the supplementary materials,

• a list of errors in my articles.

My papers can also be found on arXiv, Google Scholar and ORCID.

Most of the code reproducing the results of my articles is available on GitHub.

My PhD thesis, entitled Contributions computationnelles à la statistique Bayésienne, written under the supervision of Christian P. Robert and defended in September 2012, can be found here.

List of papers

[1]

Deligiannidis, G., Jacob, P. E., Khribch, E. M. and Wang, G. (2025). On importance sampling and independent Metropolis-Hastings with an unbounded weight function. arXiv preprint arXiv:2411.09514v2.

[2]

Douc, R., Jacob, P. E., Lee, A. and Vats, D. (2025). Solving the Poisson equation using coupled Markov chains. The Annals of Statistics (just accepted).

[3]

Atchadé, Y. F. and Jacob, P. E. (2025+). Unbiased Markov Chain Monte Carlo: what, why, and how. To appear in the Handbook of Markov chain Monte Carlo (2nd edition).

[4]

Dai, C., Heng, J., Jacob, P. E. and Whiteley, N. (2022). An invitation to sequential Monte Carlo samplers. Journal of the American Statistical Association 117 1587–600.

[5]

Kishore, N., Taylor, A. R., Jacob, P. E., Vembar, N., Cohen, T., Buckee, C. O. and Menzies, N. A. (2022). Evaluating the reliability of mobility metrics from aggregated mobile phone data as proxies for SARS-CoV-2 transmission in the USA: a population-based study. The Lancet Digital Health 4 e27–36.

[6]

Biswas, N., Bhattacharya, A., Jacob, P. E. and Johndrow, J. E. (2022). Coupling-based Convergence Assessment of some Gibbs Samplers for High-Dimensional Bayesian Regression with Shrinkage Priors. Journal of the Royal Statistical Society Series B: Statistical Methodology 84 973–96.

[7]

Buchholz, A., Chopin, N. and Jacob, P. E. (2021). Adaptive tuning of Hamiltonian Monte Carlo within sequential Monte Carlo. Bayesian Analysis.

[8]

Jacob, P. E., Gong, R., Edlefsen, P. T. and Dempster, A. P. (2021). A Gibbs Sampler for a Class of Random Convex Polytopes. Journal of the American Statistical Association 116 1181–92.

[9]

Ju, N., Heng, J. and Jacob, P. E. (2021). Sequential Monte Carlo algorithms for agent-based models of disease transmission. arXiv preprint arXiv:2101.12156.

[10]

Wang, G., O’Leary, J. and Jacob, P. (2021). Maximal Couplings of the Metropolis-Hastings Algorithm. In International conference on artificial intelligence and statistics pp 1225–33. PMLR.

[11]

Nadjahi, K., Durmus, A., Jacob, P. E., Badeau, R. and Simsekli, U. (2021). Fast approximation of the sliced-wasserstein distance using concentration of random projections. Advances in Neural Information Processing Systems 34 12411–24.

[12]

Pompe, E. and Jacob, P. E. (2021). Asymptotics of cut distributions and robust modular inference using Posterior Bootstrap. arXiv preprint arXiv:2110.11149.

[13]

Jacob, P. E., O’Leary, J. and Atchadé, Y. F. (2020). Unbiased Markov chain Monte Carlo methods with couplings. Journal of the Royal Statistical Society Series B 82 543–600.

[14]

Middleton, L., Deligiannidis, G., Doucet, A. and Jacob, P. E. (2020). Unbiased Markov chain Monte Carlo for intractable target distributions. Electronic Journal of Statistics 14 2842–91.

[15]

Ju, N., Biswas, N., Jacob, P., Mena, G., O’Leary, J. and Pompe, E. (2020). Contributed discussion on "A unified framework for de-duplication and population size estimation". Bayesian Analysis 15.

[16]

Heng, J., Jacob, P. E. and Ju, N. (2020). A simple Markov chain for independent Bernoulli variables conditioned on their sum. arXiv preprint arXiv:2012.03103.

[17]

Jacob, P. E. (2020). Couplings and monte carlo. Lecture Notes.

[18]

Jacob, P. E., Lindsten, F. and Schön, T. B. (2020). Smoothing with couplings of conditional particle filters. Journal of the American Statistical Association 115 721–9.

[19]

Heng, J. and Jacob, P. E. (2019). Unbiased Hamiltonian Monte Carlo with couplings. Biometrika 106 287–302.

[20]

Shao, S., Jacob, P. E., Ding, J. and Tarokh, V. (2019). Bayesian model comparison with the Hyvärinen score: Computation and consistency. Journal of the American Statistical Association 114 1826–37.

[21]

Lin, A., Zhang, Y., Heng, J., Allsop, S. A., Tye, K. M., Jacob, P. E. and Ba, D. (2019). Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach. In The 22nd international conference on artificial intelligence and statistics pp 2476–84.

[22]

Bernton, E., Jacob, P. E., Gerber, M. and Robert, C. P. (2019). On parameter estimation with the Wasserstein distance. Information and Inference: A Journal of the IMA 8 657–76.

[23]

Middleton, L., Deligiannidis, G., Doucet, A. and Jacob, P. E. (2019). Unbiased Smoothing using Particle Independent Metropolis-Hastings. Proceedings of Machine Learning Research, PMLR 89 2378–87.

[24]

Taylor, A. R., Jacob, P. E., Neafsey, D. E. and Buckee, C. O. (2019). Estimating relatedness between malaria parasites. Genetics 212 1337–51.

[25]

Bernton, E., Jacob, P. E., Gerber, M. and Robert, C. P. (2019). Approximate Bayesian computation with the Wasserstein distance. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 81 235–69.

[26]

Biswas, N., Jacob, P. E. and Vanetti, P. (2019). Estimating convergence of Markov chains with L-lag couplings. In Advances in neural information processing systems pp 7391–401.

[27]

Bernton, E., Heng, J., Doucet, A. and Jacob, P. E. (2019). Schrödinger Bridge Samplers. arXiv preprint arXiv:1912.13170.

[28]

Rischard, M., Jacob, P. E. and Pillai, N. (2018). Unbiased estimation of log normalizing constants with applications to Bayesian cross-validation. arXiv preprint arXiv:1810.01382.

[29]

Bernton, E., Jacob, P. E., Gerber, M. and Robert, C. P. (2017). Inference in generative models using the Wasserstein distance. arXiv preprint arXiv:1701.05146.

[30]

Jacob, P. E., Alavi, S. M. M., Mahdi, A., Payne, S. J. and Howey, D. A. (2017). Bayesian inference in non-Markovian state-space models with applications to battery fractional-order systems. IEEE Transactions on Control Systems Technology 26 497–506.

[31]

Jacob, P. E. and Funk, S. (2017). RBi: R interface to LibBi. R package.

[32]

Jacob, P. E., Murray, L. M., Holmes, C. C. and Robert, C. P. (2017). Better together? Statistical learning in models made of modules. arXiv preprint arXiv:1708.08719.

[33]

Murray, L. M., Lee, A. and Jacob, P. E. (2016). Parallel resampling in the particle filter. Journal of Computational and Graphical Statistics 25 789–805.

[34]

Jacob, P. E., Lindsten, F. and Schön, T. B. (2016). Coupling of Particle Filters. arXiv preprint arXiv:1606.01156.

[35]

Murray, L. M., Singh, S., Jacob, P. E. and Lee, A. (2016). Anytime Monte Carlo. arXiv preprint arXiv:1612.03319.

[36]

Jacob, P. E. and Thiery, A. H. (2015). On non-negative unbiased estimators. Annals of Statistics 43 769–84.

[37]

Jacob, P. E. (2015). Sequential Bayesian inference for implicit hidden Markov models and current limitations. ESAIM: Proceedings and Surveys 51 24–48.

[38]

Alavi, S., Mahdi, A., Jacob, P. E., Payne, S. J. and Howey, D. A. (2015). Structural Identifiability Analysis of Fractional Order Models with Applications in Battery Systems. arXiv preprint arXiv:1511.01402.

[39]

Jacob, P. E. and Ryder, R. J. (2014). The Wang-Landau algorithm reaches the Flat Histogram criterion in finite time. Annals of Applied Probability 24 34–53.

[40]

Bornn, L., Jacob, P. E., Del Moral, P. and Doucet, A. (2013). An Adaptive Interacting Wang-Landau Algorithm for Automatic Density Exploration. Journal of Computational and Graphical Statistics 22 749–73.

[41]

Chopin, N., Jacob, P. E. and Papaspiliopoulos, O. (2013). SMC^2: an efficient algorithm for sequential analysis of state-space models. Journal of the Royal Statistical Society: Series B 75 397–426.

[42]

Del Moral, P., Jacob, P. E., Lee, A., Murray, L. and Peters, G. W. (2013). Feynman-Kac particle integration with geometric interacting jumps. Stochastic Analysis and Applications 31 830–71.

[43]

Doucet, A., Jacob, P. E. and Rubenthaler, S. (2013). Derivative-Free Estimation of the Score Vector and Observed Information Matrix with Application to State-Space Models. arXiv preprint arXiv:1304.5768.

[44]

Jacob, P. E., Murray, L. M. and Rubenthaler, S. (2013). Path storage in the particle filter. Statistics and Computing 25 487–96.

[45]

Jacob, P. E., Robert, C. P. and Smith, M. H. (2011). Using Parallel Computation to Improve Independent Metropolis–Hastings Based Estimation. Journal of Computational and Graphical Statistics 20 616–35.

[46]

Robert, C. P., Marin, J.-M., Johansen, A. M., Jacob, P., Doucet, A., Chopin, N., Beffy, M. and Barthelme, S. (2011). Discussions on "Riemann manifold Langevin and Hamiltonian Monte Carlo methods".

[47]

Chopin, N. and Jacob, P. E. (2010). Free energy Sequential Monte Carlo, application to mixture modelling. In Bayesian statistics 9: Proceedings of the ninth valencia international meeting.) pp 91–118.

[48]

Jacob, P., Chopin, N., Robert, C. P. and Rue, H. (2009). Comments on "Particle Markov chain Monte Carlo" by C. Andrieu, A. Doucet, and R. Hollenstein. Arxiv preprint arXiv:0911.0985.

Supplementary materials

• Supplementary materials for “An Invitation to Sequential Monte Carlo Samplers” (with Chenguang Dai, Jeremy Heng, Nick Whiteley).

• Supplementary materials for “On parameter estimation with the Wasserstein distance” (with Espen Bernton, Mathieu Gerber, Christian Robert).

• Supplementary materials for “Inference in generative models using the Wasserstein distance” (with Espen Bernton, Mathieu Gerber, Christian Robert). Corresponds to arXiv v2.

• Supplementary materials for “Bayesian model comparison with the Hyvärinen score: computation and consistency” (with Stephane Shao, Jie Ding, Vahid Tarokh). Corresponds to arXiv v1.

• Supplementary materials for “SMC²: an efficient algorithm for sequential analysis of state-space models” (with Nicolas Chopin, Omiros Papaspiliopoulos).

Erratum

• There is an erratum in the rejoinder of Jacob, P. E., O’Leary, J. and Atchadé, Y. F. (2020), “Unbiased Markov chain Monte Carlo with couplings” published in the Journal of the Royal Statistical Society: Series B in 2020. The error and its fix are describe in this blog post.

• There are errors in the numerical results of “Coupling-based convergence assessment of some Gibbs samplers for high-dimensional Bayesian regression with shrinkage priors” paper. The code on GitHub has been fixed.

• There was an error in the L-lag coupling article, in the implementation of Langevin Monte Carlo. We didn’t use the correct scaling of the step size with respect to dimension. Explanations and updated code are here. I am grateful to Tamás Papp and Chris Sherlock for pointing this out.

• There were errors in the code that produced some of the figures in “Maximal Couplings of the Metropolis-Hastings Algorithm”, as pointed out by Adrien Corenflos (thanks!).

• There is an error in the SMC² article. Around Equation (3) , which gives the likelihood estimator obtained by particle filters, we wrongly write that particle filters provide unbiased estimators of \(p(y_t|y_{1:t-1},\theta)\) for all times \(t\). This is not true. Instead, particle filters provide unbiased estimators of the marginal likelihood \(p(y_1,...y_t|\theta)\).

• There was a bug in the code of the first two versions of the article “Smoothing with Couplings of Conditional Particle Filters”. The bug was fixed and the latest version on arXiv (v3) contains the updated figures.

• There was errors in the numerical results of the Unbiased HMC paper, which resulted in a published erratum at Biometrika. The erratum reads: “The values in the fifth column of Table 1, labelled ‘Rel. ineff.’, should be multiplied by a factor of 3.105691. The penultimate sentence in § 5.3 should consequently be changed to: Our guideline for choosing \(k\) and \(m\) results in a relative inefficiency of 3.26 at an average computational cost of 3518 applications of \(K_{\varepsilon,L,\sigma}\)⁠, or approximately 5 minutes of computing time with our implementation. We are grateful to Kai Xu and Hong Ge for pointing out this transcription error.”