Workflow of a typical SBI with neural posterior estimation (NPE), i.e., directly estimating the posterior. The forward modeling process transforms parameters to be inferred to data vector, and therefore includes the process of measuring summary statistics from simulation results in our case. The density estimator takes parameter-data pair {θ,d} as input, and after the training procedure returns an estimation of posterior. Note: for the neural likelihood estimation (NLE) and neural ratio estimation (NRE), the target to be trained (shown in green block) is the likelihood and the ratio of likelihood and prior, respectively. Combined with the prior, this allows us to easily build the posterior using Bayes law.