pymc-devs · twiecki · Sep 15, 2020 · Mar 16, 2020 · Mar 16, 2020 · Mar 16, 2020
diff --git a/RELEASE-NOTES.md b/RELEASE-NOTES.md
@@ -11,6 +11,7 @@
 
 ### New features
 - `sample_posterior_predictive_w` can now feed on `xarray.Dataset` - e.g. from `InferenceData.posterior`. (see [#4042](https://github.com/pymc-devs/pymc3/pull/4042))
+- Add MLDA, a new stepper for multilevel sampling. MLDA can be used when a hierarchy of approximate posteriors of varying accuracy is available, offering improved sampling efficiency especially in high-dimensional problems and/or where gradients are not available (see [#3926](https://github.com/pymc-devs/pymc3/pull/3926))
 
 
 ## PyMC3 3.9.3 (11 August 2020)

diff --git a/docs/source/notebooks/MLDA_gravity_surveying.ipynb b/docs/source/notebooks/MLDA_gravity_surveying.ipynb
diff --git a/docs/source/notebooks/MLDA_simple_linear_regression.ipynb b/docs/source/notebooks/MLDA_simple_linear_regression.ipynb
diff --git a/docs/source/notebooks/table_of_contents_examples.js b/docs/source/notebooks/table_of_contents_examples.js
@@ -58,5 +58,7 @@ Gallery.contents = {
     "GLM-hierarchical-advi-minibatch": "Variational Inference",
     "ODE_with_manual_gradients": "Inference in ODE models",
     "ODE_API_introduction": "Inference in ODE models",
-    "probabilistic_matrix_factorization": "Case Studies"
+    "probabilistic_matrix_factorization": "Case Studies",
+    "MLDA_simple_linear_regression": "MCMC",
+    "MLDA_gravity_surveying": "MCMC"
 }
diff --git a/pymc3/step_methods/__init__.py b/pymc3/step_methods/__init__.py
@@ -21,6 +21,7 @@
 from .metropolis import BinaryMetropolis
 from .metropolis import BinaryGibbsMetropolis
 from .metropolis import CategoricalGibbsMetropolis
+from .mlda import MLDA, MetropolisMLDA, RecursiveDAProposal
 from .metropolis import NormalProposal
 from .metropolis import CauchyProposal
 from .metropolis import LaplaceProposal

diff --git a/pymc3/step_methods/metropolis.py b/pymc3/step_methods/metropolis.py
@@ -577,7 +577,7 @@ def __init__(self, vars=None, S=None, proposal_dist=None, lamb=None, scaling=0.0
 
         if S is None:
             S = np.ones(model.ndim)
-        
+
         if proposal_dist is not None:
             self.proposal_dist = proposal_dist(S)
         else: