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Persistent Sampling: Enhancing the Efficiency of Sequential Monte Carlo
Minas Karamanis, Uroš Seljak
Sequential Monte Carlo (SMC) samplers are powerful tools for Bayesian inference but suffer from high computational costs due to their reliance on large particle ensembles for accur…
Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference
Richard D. P. Grumitt, Biwei Dai, Uros Seljak
We propose a general purpose Bayesian inference algorithm for expensive likelihoods, replacing the stochastic term in the Langevin equation with a deterministic density gradient te…
Uncertainty Quantification with Generative Models
Vanessa Böhm, François Lanusse, Uroš Seljak
We develop a generative model-based approach to Bayesian inverse problems, such as image reconstruction from noisy and incomplete images. Our framework addresses two common challen…
Generative Learning of Counterfactual for Synthetic Control Applications in Econometrics
Chirag Modi, Uros Seljak
A common statistical problem in econometrics is to estimate the impact of a treatment on a treated unit given a control sample with untreated outcomes. Here we develop a generative…
Posterior inference unchained with EL_2O
Uros Seljak, Byeonghee Yu
Statistical inference of analytically non-tractable posteriors is a difficult problem because of marginalization of correlated variables and stochastic methods such as MCMC and VI…