Benchmark tasks — tasks • neuralsbi

Standard simulation-based-inference benchmark tasks, following the definitions in the sbibm benchmark suite. Each task bundles a prior, a simulator, and (where one exists) an analytic reference posterior, so the same object drives unit tests, calibration studies, and the benchmark harness in inst/benchmarks/.

task_gaussian_linear() – conjugate Gaussian; analytic posterior.
task_two_moons() – crescent-shaped, bimodal posterior.
task_slcp() – 5 parameters, 8-dimensional data, strongly non-Gaussian posterior.
task_sir() – SIR epidemic dynamics with log-normal priors; no closed-form posterior (verify with SBC / coverage).

Usage

task_gaussian_linear(dim = 10L, prior_var = 0.1, noise_var = 0.1)

task_two_moons()

task_slcp()

task_sir(N = 1e6, days = 160, n_points = 10L, n_obs_draws = 1000L)

Arguments

dim: Parameter/data dimension for the Gaussian linear task (default 10).
prior_var, noise_var: Prior and likelihood variances for the Gaussian linear task.
N, days, n_points, n_obs_draws: SIR task: population size, horizon in days, number of observation times, and binomial trials per observation.

Value

A list of class nsbi_task with elements name, prior, simulator, dim_theta, dim_x, and optionally reference_posterior(x_obs, n) returning exact posterior draws.