5 citations · 15 across the 10 of their papers we have counts for
10 papers
Concentration and Calibration in Predictive Bayesian Inference
David T. Frazier, Hui Wang
Predictive Bayesian inference (PBI) represents a model-and prior-agnostic approach to standard Bayesian inference which allows users to quantify uncertainty for a functional of int…
Optimization-centric cutting feedback for semiparametric models
Linda S. L. Tan, David J. Nott, David T. Frazier
Complex statistical models are often built by combining multiple submodels, called modules. Here we consider modular inference where the modules contain both parametric and nonpara…
Robustifying Approximate Bayesian Computation
Chaya Weerasinghe, David T. Frazier, Ruben Loaiza-Maya +1
Approximate Bayesian computation (ABC) is one of the most popular "likelihood-free" methods. These methods have been applied in a wide range of fields by providing solutions to int…
Simulation-based Bayesian inference under model misspecification
Ryan P. Kelly, David J. Warne, David T. Frazier +3
Simulation-based Bayesian inference (SBI) methods are widely used for parameter estimation in complex models where evaluating the likelihood is challenging but generating simulatio…
The Statistical Accuracy of Neural Posterior and Likelihood Estimation
David T. Frazier, Ryan Kelly, Christopher Drovandi +1
Neural posterior estimation (NPE) and neural likelihood estimation (NLE) are machine learning approaches that provide accurate posterior, and likelihood, approximations in complex…
Exact Sampling of Gibbs Measures with Estimated Losses
David T. Frazier, Jeremias Knoblauch, Jack Jewson +1
A popular strategy for ameliorating some of the shortcomings of Bayesian posterior inference is to instead target a Gibbs measure based on losses that connect a parameter of intere…
Calibrated Generalized Bayesian Inference
David T. Frazier, Christopher Drovandi, Robert Kohn
We propose a simple approach that provides accurate uncertainty quantification for Bayesian inference in misspecified or approximate models, and for generalized (Gibbs) posteriors.…
Cutting Feedback in Misspecified Copula Models
Michael Stanley Smith, Weichang Yu, David J. Nott +1
In copula models the marginal distributions and copula function are specified separately. We treat these as two modules in a modular Bayesian inference framework, and propose condu…
Component-wise iterative ensemble Kalman inversion for static Bayesian models with unknown measurement error covariance
Imke Botha, Matthew P. Adams, Dang Khuong Tran +2
The ensemble Kalman filter (EnKF) is a Monte Carlo approximation of the Kalman filter for high dimensional linear Gaussian state space models. EnKF methods have also been developed…
Bayesian inference using synthetic likelihood: asymptotics and adjustments
David T. Frazier, David J. Nott, Christopher Drovandi +1
Implementing Bayesian inference is often computationally challenging in applications involving complex models, and sometimes calculating the likelihood itself is difficult. Synthet…