12 citations as recorded by crossref.

1. Iterative Probabilistic Approach for Selection of Time-Varying Model Classes K. Hoi et al. 10.1016/j.proeng.2011.07.325
2. Inverse modelling of cloud-aerosol interactions – Part 2: Sensitivity tests on liquid phase clouds using a Markov chain Monte Carlo based simulation approach D. Partridge et al. 10.5194/acp-12-2823-2012
3. Retrieval of atmospheric CH4profiles from Fourier transform infrared data using dimension reduction and MCMC S. Tukiainen et al. 10.1002/2015JD024657
4. ‘Bayesian source detection and parameter estimation of a plume model based on sensor network measurements’ by C. Huang et al.: Rejoinder C. Huang et al. 10.1002/asmb.858
5. Eclipse time variations in the post-common envelope binary V470 Cam O. Sale et al. 10.1093/mnras/staa3013
6. Assessment of the proarrhythmic effects of repurposed antimalarials for COVID-19 treatment using a comprehensive in vitro proarrhythmia assay (CiPA) S. Yoon et al. 10.3389/fphar.2023.1220796
7. Inverse modeling of cloud-aerosol interactions – Part 1: Detailed response surface analysis D. Partridge et al. 10.5194/acp-11-7269-2011
8. MECCO: A method to estimate concentrations of condensing organics—Description and evaluation of a Markov chain Monte Carlo application H. Vuollekoski et al. 10.1016/j.jaerosci.2010.09.004
9. Estimating the variability of contact parameter temperature dependence with the Monte Carlo Markov Chain method A. Määttänen & M. Douspis 10.1016/j.grj.2014.09.002
10. Trans-dimensional Bayesian inversion of airborne transient EM data from Taylor Glacier, Antarctica D. Blatter et al. 10.1093/gji/ggy255
11. Convergence of adaptive and interacting Markov chain Monte Carlo algorithms G. Fort et al. 10.1214/11-AOS938
12. A general purpose sampling algorithm for continuous distributions (the t-walk) J. Christen & C. Fox 10.1214/10-BA603