4 papers
A thermodynamic approach to Approximate Bayesian Computation with multiple summary statistics
Carlo Albert, Simone Ulzega, Simon Dirmeier +3
Bayesian inference with stochastic models is often difficult because their likelihood functions involve high-dimensional integrals. Approximate Bayesian Computation (ABC) avoids ev…
Simulation-based Inference with the Python Package sbijax
Simon Dirmeier, Antonietta Mira, Carlo Albert
Neural simulation-based inference (SBI) describes an emerging family of methods for Bayesian inference with intractable likelihood functions that use neural networks as surrogate m…
Simulation-based Inference for High-dimensional Data using Surjective Sequential Neural Likelihood Estimation
Simon Dirmeier, Carlo Albert, Fernando Perez-Cruz
Neural likelihood estimation methods for simulation-based inference can suffer from performance degradation when the modeled data is very high-dimensional or lies along a lower-dim…
Causal Posterior Estimation
Simon Dirmeier, Antonietta Mira
We present Causal Posterior Estimation (CPE), a novel method for Bayesian inference in simulator models, i.e., models where the evaluation of the likelihood function is intractable…