Please find below a list of my publications. If you have issues accessing them, please send me an email: jl2160[obvious symbol]


[2p] Jonas Latz, Juan P. Madrigal-Cianci, Fabio Nobile, Raul Tempone (2020): Generalized Parallel Tempering on Bayesian Inverse Problems. (.bib, arXiv)

[1p] Felipe Uribe, Iason Papaioannou, Jonas Latz, Wolfgang Betz, Elisabeth Ullmann, Daniel Straub (2020): Bayesian inference with subset simulation in varying dimensions applied to the Karhunen–Loève expansion. (.bib, full text)

Refereed journal articles

[10] Jonas Latz (2021): Analysis of Stochastic Gradient Descent in Continuous Time. Statistics and Computing (accepted). (.bib, arXiv)

[9] Fabian Wagner, Jonas Latz, Iason Papaioannou, Elisabeth Ullmann (2021): Error analysis for probabilities of rare events with approximate models. SIAM Journal on Numerical Analysis (accepted). (.bib, arXiv)

[8] Jeremy Budd, Yves van Gennip, Jonas Latz (2021): Classification and image processing with a semi-discrete scheme for fidelity forced Allen–Cahn on graphs. GAMM Mitteilungen 44(1), e202100004, doi. (.bib, arXiv)

[7] Daniel Kressner, Jonas Latz, Stefano Massei, Elisabeth Ullmann (2020): Certified and fast computations with shallow covariance kernels. Foundations of Data Science 2(4), pp. 487–512, doi. (.bib, arXiv)

[6] Fabian Wagner, Jonas Latz, Iason Papaioannou, Elisabeth Ullmann (2020): Multilevel Sequential Importance Sampling for Rare Event Estimation. SIAM Journal on Scientific Computing 42(4), pp. A2062–A2087, doi. (.bib, arXiv)

[5] Jonas Latz (2020): On the Well-posedness of Bayesian Inverse Problems. SIAM/ASA Journal on Uncertainty Quantification 8(1), pp. 451–482, doi. (.bib, arXiv)

[4] Ionuţ-Gabriel Farcaş, Jonas Latz, Elisabeth Ullmann, Tobias Neckel, Hans-Joachim Bungartz (2020): Multilevel Adaptive Sparse Leja Approximations for Bayesian Inverse Problems. SIAM Journal on Scientific Computing 42(1), pp. A424–A451, doi. (.bib, arXiv)

[3] Christian Kahle, Kei Fong Lam, Jonas Latz, Elisabeth Ullmann (2019): Bayesian parameter identification in Cahn-Hilliard models for biological growth. SIAM/ASA Journal on Uncertainty Quantification 7(2), pp. 526-552, doi. (.bib, arXiv)

[2] Jonas Latz, Marvin Eisenberger, Elisabeth Ullmann (2019): Fast Sampling of parameterised Gaussian random fields. Computer Methods in Applied Mechanics and Engineering 348, pp. 978-1012, doi. (.bib, arXiv)

[1] Jonas Latz, Iason Papaioannou, Elisabeth Ullmann (2018): Multilevel Sequential² Monte Carlo for Bayesian Inverse Problems. Journal of Computational Physics 368, pp. 154-178, doi. (.bib, arXiv)

Refereed book chapters and articles in conference proceedings

[1b] Matthieu Bulté, Jonas Latz, Elisabeth Ullmann (2020): A practical example for the non-linear Bayesian filtering of model parameters. in M. D’Elia, M. Gunzburger, G. Rozza (ed.): Quantification of Uncertainty: Improving Efficiency and Technology - QUIET selected contributions, Lecture Notes in Computational Science and Engineering Vol. 137, Springer, Cham, Chpt. 11, pp. 241-272, doi. (github, .bib, arXiv)


[3t] Jonas Latz (2019): Exploring and exploiting hierarchies in Bayesian inverse problems. Doctoral thesis, Technical University of Munich. (.bib, full text)

[2t] Jonas Latz (2016): Bayes Linear Methods for Inverse Problems. Master’s thesis, University of Warwick. (.bib, full text)

[1t] Jonas Latz (2014): Äußere Hausdorff-Maße: Anwendungen und Eigenschaften. Bachelor’s thesis, University of Trier (in German).

Miscellaneous (non-refereed)

[1n] Jonas Latz (2019): On the well-posedness of Bayesian inverse problems: The Gaussian noise case. Oberwolfach Report 12/2019, pp. 35-36, doi.