Bayesian inference is a powerful tool for combining information in complex settings, a task of
increasing importance in modern applications. However, Bayesian inference with a flawed …

Simulation-based inference (SBI) methods such as approximate Bayesian computation
(ABC), synthetic likelihood, and neural posterior estimation (NPE) rely on simulating …

Computer simulations have proven a valuable tool for understanding complex phenomena
across the sciences. However, the utility of simulators for modelling and forecasting …

Aided by advances in neural density estimation, considerable progress has been made in
recent years towards a suite of simulation-based inference (SBI) methods capable of …

Probabilistic (Bayesian) modeling has experienced a surge of applications in almost all
quantitative sciences and industrial areas. This development is driven by a combination of …

Recent advances in probabilistic deep learning enable efficient amortized Bayesian
inference in settings where the likelihood function is only implicitly defined by a simulation …

This paper takes the reader on a journey through the history of Bayesian computation, from
the 18th century to the present day. Beginning with the one-dimensional integral first …

Likelihood-free methods are useful for parameter estimation of complex models with
intractable likelihood functions for which it is easy to simulate data. Such models are …

The 21st century has seen an enormous growth in the development and use of approximate
Bayesian methods. Such methods produce computational solutions to certain “intractable” …

Recent advances in probabilistic deep learning enable efficient amortized Bayesian
inference in settings where the likelihood function is only implicitly defined by a simulation …