Approximate Inference

Approximate Bayesian Computation

Introduced by Kulkarni et al. in Accelerating Simulation-based Inference with Emerging AI Hardware

Class of methods in Bayesian Statistics where the posterior distribution is approximated over a rejection scheme on simulations because the likelihood function is intractable.

Different parameters get sampled and simulated. Then a distance function is calculated to measure the quality of the simulation compared to data from real observations. Only simulations that fall below a certain threshold get accepted.

Image source: Kulkarni et al.

Source: Accelerating Simulation-based Inference with Emerging AI Hardware

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