8 citations · 9 across the 5 of their papers we have counts for
5 papers
Normalizing Flow Regression for Bayesian Inference with Offline Likelihood Evaluations
Chengkun Li, Bobby Huggins, Petrus Mikkola +1
Bayesian inference with computationally expensive likelihood evaluations remains a significant challenge in many scientific domains. We propose normalizing flow regression (NFR), a…
Stacking Variational Bayesian Monte Carlo
Francesco Silvestrin, Chengkun Li, Luigi Acerbi
Approximate Bayesian inference for models with computationally expensive, black-box likelihoods poses a significant challenge, especially when the posterior distribution is complex…
Amortized Bayesian Workflow
Chengkun Li, Aki Vehtari, Paul-Christian Bürkner +3
Bayesian inference often faces a trade-off between computational speed and sampling accuracy. We propose an adaptive workflow that integrates rapid amortized inference with gold-st…
PyVBMC: Efficient Bayesian inference in Python
Bobby Huggins, Chengkun Li, Marlon Tobaben +2
PyVBMC is a Python implementation of the Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference for black-box computational models (Acerbi, 2018, 2020)…
Fast post-process Bayesian inference with Variational Sparse Bayesian Quadrature
Chengkun Li, Grégoire Clarté, Martin Jørgensen +1
In applied Bayesian inference scenarios, users may have access to a large number of pre-existing model evaluations, for example from maximum-a-posteriori (MAP) optimization runs. H…