most citedDemonstrating the power and flexibility of variational assumptions for amortized neural posterior estimation in environmental applications

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stat.CO2026★ 1 cited

Demonstrating the power and flexibility of variational assumptions for amortized neural posterior estimation in environmental applications

Elliot Maceda, Emily C. Hector, Amanda Lenzi +1

Classic Bayesian methods with complex models are frequently infeasible due to an intractable likelihood. Simulation-based inference methods, such as Approximate Bayesian Computing…