5 citations · 6 across the 3 of their papers we have counts for
3 papers
Faking feature importance: A cautionary tale on the use of differentially-private synthetic data
Oscar Giles, Kasra Hosseini, Grigorios Mingas +13
Synthetic datasets are often presented as a silver-bullet solution to the problem of privacy-preserving data publishing. However, for many applications, synthetic data has been sho…
Multilevel Delayed Acceptance MCMC
Mikkel B. Lykkegaard, Tim J. Dodwell, Colin Fox +2
We develop a novel Markov chain Monte Carlo (MCMC) method that exploits a hierarchy of models of increasing complexity to efficiently generate samples from an unnormalized target d…
Multilevel Delayed Acceptance MCMC with an Adaptive Error Model in PyMC3
Mikkel B. Lykkegaard, Grigorios Mingas, Robert Scheichl +2
Uncertainty Quantification through Markov Chain Monte Carlo (MCMC) can be prohibitively expensive for target probability densities with expensive likelihood functions, for instance…