diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index 22dbbacc..0bc4ea54 100644
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -1,6 +1,9 @@
 Changelog
 =========
 
+
+0.7.7 (2020-10-12)
+------------------
 - Update info to reflect setting python 3.6 as the default version
 - Update documentation to setting python 3.6 as default
 - Add dask support to elfi client options
diff --git a/README.md b/README.md
index f0fe99bf..1f5c3848 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-**Version 0.7.6 released!** See the [CHANGELOG](CHANGELOG.rst) and [notebooks](https://github.com/elfi-dev/notebooks).
+**Version 0.7.7 released!** See the [CHANGELOG](CHANGELOG.rst) and [notebooks](https://github.com/elfi-dev/notebooks).
 
 **NOTE:** For the time being NetworkX 2 is incompatible with ELFI.
 
diff --git a/elfi/__init__.py b/elfi/__init__.py
index 6a5c7a15..7f854c2d 100644
--- a/elfi/__init__.py
+++ b/elfi/__init__.py
@@ -26,4 +26,4 @@
 __email__ = 'elfi-support@hiit.fi'
 
 # make sure __version_ is on the last non-empty line (read by setup.py)
-__version__ = '0.7.6'
+__version__ = '0.7.7'
diff --git a/elfi/clients/dask.py b/elfi/clients/dask.py
index 2a84303c..134d6a13 100644
--- a/elfi/clients/dask.py
+++ b/elfi/clients/dask.py
@@ -56,11 +56,11 @@ def get_result(self, task_id):
         Parameters
         ----------
         task_id: int
-        
+
         Returns
         -------
         dict
-        
+
         """
         async_result = self.tasks.pop(task_id)
         return async_result.result()
diff --git a/elfi/methods/post_processing.py b/elfi/methods/post_processing.py
index b1efb741..b4c9a39c 100644
--- a/elfi/methods/post_processing.py
+++ b/elfi/methods/post_processing.py
@@ -242,8 +242,8 @@ def adjust_posterior(sample, model, summary_names, parameter_names=None, adjustm
     >>> import elfi
     >>> from elfi.examples import gauss
     >>> m = gauss.get_model()
-    >>> res = elfi.Rejection(m['d'], output_names=['ss_mean', 'ss_var'],\
-      batch_size=10).sample(500, bar=False)
+    >>> res = elfi.Rejection(m['d'], output_names=['ss_mean', 'ss_var'],
+    ...                      batch_size=10).sample(500, bar=False)
     >>> adj = adjust_posterior(res, m, ['ss_mean', 'ss_var'], ['mu'], LinearAdjustment())
 
     """