6 papers
Uncertainty Decomposition for Bayes-Filtered Transformers via Bayesian Predictive Inference
Sandra Fortini, Kenyon Ng, Sonia Petrone +2
Bayes-filtered transformers are transformers meta-learned on sequences from a prior predictive distribution to approximate the corresponding posterior predictive distribution. They…
Persuasive Privacy
Joshua J Bon, James Bailie, Judith Rousseau +1
We propose a novel framework for measuring privacy from a Bayesian game-theoretic perspective. This framework enables the creation of new, purpose-driven privacy definitions that a…
Empirical Bayes in Bayesian learning: understanding a common practice
Stefano Rizzelli, Judith Rousseau, Sonia Petrone
In applications of Bayesian procedures, once a class of priors has been chosen, it may be tempting to fix the prior's hyperparameters from the data, in an empirical Bayes (EB) fash…
Asymptotics of approximate Bayesian computation when summary statistics converge at heterogeneous rates
Caroline Lawless, Christian P. Robert, Judith Rousseau +1
We consider the asymptotic properties of Approximate Bayesian Computation (ABC) for the realistic case of summary statistics with heterogeneous rates of convergence. We allow some…
Asymptotic Properties of Approximate Bayesian Computation
David T. Frazier, Gael M. Martin, Christian P. Robert +1
Approximate Bayesian computation allows for statistical analysis in models with intractable likelihoods. In this paper we consider the asymptotic behaviour of the posterior distrib…
Bayesian Inference
Christian P. Robert, Jean-Michel Marin, Judith Rousseau
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal method for summarising uncertainty and making estimates and predictions using prob…