Data assimilation is a fundamental task in updating forecasting models upon observing new data, with applications ranging from weather prediction to online reinforcement learning. Deep generative...

Simulation-based inference (SBI) enables amortized Bayesian inference by first training a neural posterior estimator (NPE) on prior-simulator pairs, typically through low-dimensional summary statistic...

A set of wrappers around rjags functions to run Bayesian analyses in JAGS (specifically, via libjags). A single function call can control adaptive, burn-in, and sampling MCMC phases, with MCMC chains ...

A popular method for likelihood-free inference is approximate Bayesian computation sequential Monte Carlo (ABC-SMC) algorithms. These approximate the posterior using a population of particles, which a...

Neural simulation-based inference (SBI) is a popular set of methods for Bayesian inference when models are only available in the form of a simulator. These methods are widely used in the sciences...

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The 2025 BioInference Conference

The Scientific Committee invites the submission of proposals for Invited Sessions & Panel Discussions for the 2026 ISBA World Meeting, to be held in Nagoya, Japan from 28 June to 3 July, 2026. Invited...

This workshop focuses on leveraging modern machine learning to accelerate statistical inference, experimental design, and scientific discovery. It features...

Petition to form a Bayesian Social Sciences section at ISBA The following members of the International Society for Bayesian Analysis (ISBA) wish to petition the ISBA board to be recognized as the IS...

Neural posterior estimation (NPE) and neural likelihood estimation (NLE) are machine learning approaches that provide accurate posterior, and likelihood, approximations in complex modeling scenarios, ...

The increasing frequency and severity of extreme events—such as catastrophic flooding, record-breaking temperatures, and unprecedented heatwaves—has ...

A Python library for amortized Bayesian workflows using generative neural networks. - GitHub - bayesflow-org/bayesflow at dev