add tutorial 15 without outputs and meta

Author: janfb

tutorials/15_importance_sampled_posteriors.ipynb

@@ -71,7 +71,7 @@
    "id": "4808d6d3-cb14-4ecd-a0f5-824bf54a5b01",
    "metadata": {},
    "source": [
-    "We first define a simulator and a prior which both have a `sample` function (as required for `sbi`) and `log_prob` evaluations (as required for importance sampling)."
+    "We first define a simulator and a prior which both have functions for sampling (as required for SBI) and log_prob evaluations (as required for importance sampling)."
    ]
   },
   {
@@ -202,10 +202,8 @@
    "id": "7150cc13-9911-4656-936f-9cafb867eba4",
    "metadata": {},
    "source": [
-    "With the `sbi` toolbox, importance sampling is a one-liner. `sbi` supports two methods\n",
-    "for importance sampling:\n",
-    "\n",
-    "- `\"importance\"`: returns `n_samples` weighted samples (as above) corresponding to `n_samples * sample_efficiency` samples from the posterior. This results in unbiased samples, but the number of effective samples may be small when the `sbi` estimate is inaccurate.\n",
+    "With the SBI toolbox, importance sampling is a one-liner. SBI supports two methods for importance sampling:\n",
+    "- `\"importance\"`: returns `n_samples` weighted samples (as above) corresponding to `n_samples * sample_efficiency` samples from the posterior. This results in unbiased samples, but the number of effective samples may be small when the SBI estimate is inaccurate.\n",
     "- `\"sir\"` (sampling-importance-resampling): performs rejection sampling on a batched basis with batch size `oversampling_factor`.  This is a guaranteed way to obtain `N / oversampling_factor` samples, but these may be biased as the weight normalization is not performed across the entire set of samples."
    ]
   },