834 citations · 3.9k across the 95 of their papers we have counts for
4 papers · 1 filter
Parallelising MCMC via Random Forests
Wu Changye, Christian P. Robert
For Bayesian computation in big data contexts, the divide-and-conquer MCMC concept splits the whole data set into batches, runs MCMC algorithms separately over each batch to produc…
Many perspectives on Deborah Mayo's "Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars"
Andrew Gelman, Brian Haig, Christian Hennig +8
The new book by philosopher Deborah Mayo is relevant to data science for topical reasons, as she takes various controversial positions regarding hypothesis testing and statistical…
Approximate Bayesian computation with the Wasserstein distance
Espen Bernton, Pierre E. Jacob, Mathieu Gerber +1
A growing number of generative statistical models do not permit the numerical evaluation of their likelihood functions. Approximate Bayesian computation (ABC) has become a popular…
Component-wise approximate Bayesian computation via Gibbs-like steps
Grégoire Clarté, Christian P. Robert, Robin Ryder +1
Approximate Bayesian computation methods are useful for generative models with intractable likelihoods. These methods are however sensitive to the dimension of the parameter space,…