diff --git a/examples/example_16/ob08092-o4_minimal_MN.yaml b/examples/example_16/ob08092-o4_minimal_MN.yaml
index d8347564..7e95e488 100644
--- a/examples/example_16/ob08092-o4_minimal_MN.yaml
+++ b/examples/example_16/ob08092-o4_minimal_MN.yaml
@@ -1,6 +1,7 @@
 photometry_files:
     data/OB08092/phot_ob08092_O4.dat
-fit_method: UltraNest  # options: EMCEE, MultiNest, UltraNest (... dynesty)
+fit_method: MultiNest
+# fit_method options if prior_limits are given: MultiNest and UltraNest
 prior_limits:
     t_0: [2455379.4, 2455379.76]
     u_0: [0.46, 0.65]
diff --git a/examples/example_16/ulens_model_fit.py b/examples/example_16/ulens_model_fit.py
index 387583fd..504b9879 100644
--- a/examples/example_16/ulens_model_fit.py
+++ b/examples/example_16/ulens_model_fit.py
@@ -1456,13 +1456,14 @@ def _parse_fitting_parameters_UltraNest(self):
 
         self._check_required_and_allowed_parameters(required, allowed)
         self._check_parameters_types(settings, bools, ints, floats, strings)
-        value = settings.pop("log directory")
-        if value is not None:
-            if path.exists(value) and path.isdir(value):
-                self._log_dir_UltraNest = value
-            else:
+        self._log_dir_UltraNest = settings.pop("log directory", None)
+        if self._log_dir_UltraNest is not None:
+            if not path.exists(self._log_dir_UltraNest):
                 raise ValueError("log directory value in fitting_parameters"
                                  "does not exist.")
+            elif not path.isdir(self._log_dir_UltraNest):
+                raise ValueError("log directory value in fitting_parameters"
+                                 "exists, but it is a file.")
         value = settings.pop("derived parameter names", "")
         self._derived_params_UltraNest = value.split()
 
@@ -2671,6 +2672,9 @@ def _run_fit_UltraNest(self):
         """
         Run Ultranest fit
         """
+        self._kwargs_UltraNest['dlogz'] = 100.  # 0.5... 100. took 15min
+        self._kwargs_UltraNest['dKL'] = 100.  # 0.5
+        self._kwargs_UltraNest['frac_remain'] = 0.005
         self._result_UltraNest = self._sampler.run(**self._kwargs_UltraNest)
 
     def _finish_fit(self):
@@ -3229,7 +3233,8 @@ def _parse_results_UltraNest(self):
         # self._samples_flat = self._result_UltraNest['samples'][:, :-2]
         # weighted samples from the posterior
         weighted_samples = self._result_UltraNest['weighted_samples']
-        self._samples_flat = weighted_samples['points'][:, :-2]
+        index = self._n_fit_parameters
+        self._samples_flat = weighted_samples['points'][:, :index]
         self._samples_flat_weights = weighted_samples['weights']
         self._sampler.print_results()