59 citations · 173 across the 29 of their papers we have counts for
11 papers · 1 filter
Markov chain Monte Carlo for predictively oriented posteriors
Yann McLatchie, Louis Sharrock, David T. Frazier +1
The predictively oriented posterior offers principled uncertainty quantification, even under model misspecification. However, it does not admit an explicit density and therefore ca…
Conjugate Generalized Bayesian Inference for Discrete Doubly Intractable Problems
William Laplante, Matias Altamirano, Jeremias Knoblauch +2
Doubly intractable problems occur when both the likelihood and the posterior are available only in unnormalized form, with computationally intractable normalization constants. Baye…
TabMGP: Martingale Posterior with TabPFN
Kenyon Ng, Edwin Fong, David T. Frazier +2
Bayesian inference provides principled uncertainty quantification but is often limited by the challenges of prior and likelihood elicitation. The martingale posterior (MGP) (Fong e…
Predictively Oriented Posteriors
Yann McLatchie, Badr-Eddine Cherief-Abdellatif, David T. Frazier +1
We advocate for a new statistical principle that combines the most desirable aspects of both parameter inference and density estimation. This leads us to the predictively oriented…
Prediction-Centric Uncertainty Quantification via MMD
Zheyang Shen, Jeremias Knoblauch, Sam Power +1
Deterministic mathematical models, such as those specified via differential equations, are a powerful tool to communicate scientific insight. However, such models are necessarily s…
Robustifying likelihoods by optimistically re-weighting data
Miheer Dewaskar, Christopher Tosh, Jeremias Knoblauch +1
Likelihood-based inferences have been remarkably successful in wide-spanning application areas. However, even after due diligence in selecting a good model for the data at hand, th…
Generalised Bayesian Inference for Discrete Intractable Likelihood
Takuo Matsubara, Jeremias Knoblauch, François-Xavier Briol +1
Discrete state spaces represent a major computational challenge to statistical inference, since the computation of normalisation constants requires summation over large or possibly…
Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap
Charita Dellaporta, Jeremias Knoblauch, Theodoros Damoulas +1
Simulator-based models are models for which the likelihood is intractable but simulation of synthetic data is possible. They are often used to describe complex real-world phenomena…
Robust Generalised Bayesian Inference for Intractable Likelihoods
Takuo Matsubara, Jeremias Knoblauch, François-Xavier Briol +1
Generalised Bayesian inference updates prior beliefs using a loss function, rather than a likelihood, and can therefore be used to confer robustness against possible mis-specificat…
Generalized Posteriors in Approximate Bayesian Computation
Sebastian M Schmon, Patrick W Cannon, Jeremias Knoblauch
Complex simulators have become a ubiquitous tool in many scientific disciplines, providing high-fidelity, implicit probabilistic models of natural and social phenomena. Unfortunate…
Robust Bayesian Inference for Discrete Outcomes with the Total Variation Distance
Jeremias Knoblauch, Lara Vomfell
Models of discrete-valued outcomes are easily misspecified if the data exhibit zero-inflation, overdispersion or contamination. Without additional knowledge about the existence and…