dingo.gw.importance_sampling package

Submodules

dingo.gw.importance_sampling.diagnostics module

dingo.gw.importance_sampling.diagnostics.plot_diagnostics(result: Result, outdir, num_processes=1, num_slice_plots=0, n_grid_slice1d=200, n_grid_slice2d=100, params_slice2d=None)

dingo.gw.importance_sampling.diagnostics.plot_posterior_slice(sampler, theta, theta_range, outname=None, num_processes=1, n_grid=200, parameters=None, normalize_conditionals=False)

dingo.gw.importance_sampling.diagnostics.plot_posterior_slice2d(sampler, theta, theta_range, n_grid=100, num_processes=1, outname=None)

dingo.gw.importance_sampling.importance_weights module

Step 1: Train unconditional nde Step 2: Set up likelihood and prior

dingo.gw.importance_sampling.importance_weights.main()

dingo.gw.importance_sampling.importance_weights.parse_args()

Module contents

Implements sampling-importance-resampling (sir) for GW posteriors.