9 papers
Deep adaptive design with an evidential bias criterion
David Chen, Michael Evans, Xinwei Li +2
Bayesian optimal experimental design (BOED) aims to collect informative data by optimizing an expected utility reflecting the goals of an experiment. However, this optimization is…
Bayesian Modular Inference for Copula Models with Potentially Misspecified Marginals
Lucas Kock, David T. Frazier, Michael Stanley Smith +1
Copula models of multivariate data are popular because they allow separate specification of marginal distributions and the copula function. These components can be treated as inter…
Predictive variational inference for flexible regression models
Lucas Kock, Scott A. Sisson, G. S. Rodrigues +1
A conventional Bayesian approach to prediction uses the posterior distribution to integrate out parameters in a density for unobserved data conditional on the observed data and par…
Detecting Conflicts in Evidence Synthesis Models Using Score Discrepancies
Fuming Yang, David J. Nott, Anne M. Presanis
Evidence synthesis models combine multiple data sources to estimate latent quantities of interest, enabling reliable inference on parameters that are difficult to measure directly.…
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…
Robust Bayesian methods using amortized simulation-based inference
Wang Yuyan, Michael Evans, David J. Nott
Bayesian simulation-based inference (SBI) methods are used in statistical models where simulation is feasible but the likelihood is intractable. Standard SBI methods can perform po…
Variational inference for hierarchical models with conditional scale and skewness corrections
Lucas Kock, Linda S. L. Tan, Prateek Bansal +1
Gaussian variational approximations are widely used for summarizing posterior distributions in Bayesian models, especially in high-dimensional settings. However, a drawback of such…
Multi-objective Bayesian optimization for Likelihood-Free inference in sequential sampling models of decision making
David Chen, Xinwei Li, Eui-Jin Kim +2
Statistical models are often defined by a generative process for simulating synthetic data, but this can lead to intractable likelihoods. Likelihood free inference (LFI) methods en…
Bayesian Synthetic Likelihood
David T. Frazier, Christopher Drovandi, David J. Nott
Bayesian statistics is concerned with conducting posterior inference for the unknown quantities in a given statistical model. Conventional Bayesian inference requires the specifica…