Simulation-Based Inference
A class of statistical inference methods that learn parameter information solely through simulated samples when the likelihood function is intractable. Widely applied in complex-model domains such as astrophysics and systems biology.
Timeline (last 90 days)
The inference accuracy of this method depends on how closely the simulator approximates the true process; if the real reaction involves mechanisms not included in the simulation, the posterior estimates may be biased.
SLICE uses an amortized neural ratio estimator to approximate the true likelihood ratio, which is the mainstream tool in the simulation-based inference field in recent years.
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The inference accuracy of this method depends on how closely the simulator approximates the true process; if the real reaction involves mechanisms not included in the simulation, the posterior estimates may be biased.
50%UnverifiedSLICE uses an amortized neural ratio estimator to approximate the true likelihood ratio, which is the mainstream tool in the simulation-based inference field in recent years.
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