SBI
A class of methods for performing Bayesian inference in settings where the likelihood function is not analytically tractable but samples can be generated through forward simulation. Mainstream techniques include ABC, NPE and NLE.
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SBI is a class of methods for Bayesian inference in settings where the likelihood is not analytically tractable but samples can be generated by forward simulation, with typical applications including cosmology, particle physics and epidemiology.
Mainstream SBI techniques such as approximate Bayesian computation, neural posterior estimation and neural likelihood estimation all approximate the posterior or likelihood by training neural networks and typically do not directly output the evidence.
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SBI is a class of methods for Bayesian inference in settings where the likelihood is not analytically tractable but samples can be generated by forward simulation, with typical applications including cosmology, particle physics and epidemiology.
50%UnverifiedMainstream SBI techniques such as approximate Bayesian computation, neural posterior estimation and neural likelihood estimation all approximate the posterior or likelihood by training neural networks and typically do not directly output the evidence.
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