Training

sbi.inference.BNRE	Balanced Neural Ratio Estimation (BNRE) as in Delaunoy et al. (2022) [1].
sbi.inference.FMPE	Flow Matching Posterior Estimation (FMPE) [1].
sbi.inference.MarginalTrainer	Utility class for training a marginal density estimator $p(x)$.
sbi.inference.MCABC	Monte-Carlo Approximate Bayesian Computation (Rejection ABC).
sbi.inference.MNLE	Mixed Neural Likelihood Estimation for discrete and continuous data [1].
sbi.inference.MNPE	Method that can infer discrete and continuous parameters (Mixed NPE).
sbi.inference.NLE_A	Neural Likelihood Estimation (NLE) as in Papamakarios et al. (2019) [1].
sbi.inference.NPE_A	Neural Posterior Estimation algorithm as in Papamakarios et al. (2016) [1].
sbi.inference.NPE_B	Neural Posterior Estimation algorithm (NPE-B) as in Lueckmann et al. (2017) [1].
sbi.inference.NPE_C	Neural Posterior Estimation algorithm (NPE-C) as in Greenberg et al. (2019) [1].
sbi.inference.NPSE	Neural Posterior Score Estimation (NPSE) [1, 2].
sbi.inference.NRE_A	Neural Ratio Estimation (NRE-A / AALR) as in Hermans et al. (2020) [1].
sbi.inference.NRE_B	Neural Ratio Estimation (NRE-B / SRE) as in Durkan et al. (2020) [1].
sbi.inference.NRE_C	Neural Ratio Estimation (NRE-C / CNRE) as in Miller et al. (2022) [1].
sbi.inference.SMCABC	Sequential Monte Carlo Approximate Bayesian Computation.