preferences-scalable-probabilistic-modeling-workflow

Simulation-based inference on stochastic dynamical systems uses a principled Bayesian workflow to validate simulators and infer parameters when likelihoods are intractable. It guides model development, fidelity checks, and posterior approximation for complex domains like epidemiology and climate science.

A principled Bayesian workflow is an iterative methodology for model development and validation. It extends standard approaches by structuring prior predictive checks, algorithmic calibration, inferential calibration, and posterior retrodictive checks to ensure complex probabilistic models are robust and reliable.

Amortized inference accelerates Bayesian posterior approximation by integrating neural networks. This allows efficient posterior estimation across many datasets without re-running full optimizations, making statistical inference for stochastic dynamical systems significantly faster and more accessible.

Yes, this Bayesian workflow applies to domains like climate science, epidemiology, and molecular biology. It specifically handles complex simulators in these fields by validating faithfulness, checking priors, and calibrating algorithms to accurately infer parameters from observational data.

Simulation-based inference for implicit likelihoods requires rigorous algorithmic and inferential calibration to avoid biased posteriors. Limitations include the computational cost of training neural networks for amortized inference and the necessity of high-fidelity simulators to ensure valid retrodictive checks.