diff --git a/bhmm/__init__.py b/bhmm/__init__.py
index f17452a..aa2ef92 100644
--- a/bhmm/__init__.py
+++ b/bhmm/__init__.py
@@ -14,12 +14,12 @@
 
 # hmms
 from bhmm.hmm.generic_hmm import HMM
-#from bhmm.hmm.gaussian_hmm import GaussianHMM
-#from bhmm.hmm.discrete_hmm import DiscreteHMM
+from bhmm.hmm.gaussian_hmm import GaussianHMM
+from bhmm.hmm.discrete_hmm import DiscreteHMM
 
 from bhmm.hmm.generic_sampled_hmm import SampledHMM
-#from bhmm.hmm.gaussian_hmm import SampledGaussianHMM
-#from bhmm.hmm.discrete_hmm import SampledDiscreteHMM
+from bhmm.hmm.gaussian_hmm import SampledGaussianHMM
+from bhmm.hmm.discrete_hmm import SampledDiscreteHMM
 
 # estimators
 from bhmm.estimators.bayesian_sampling import BayesianHMMSampler as BHMM
diff --git a/examples/discrete_1d_2min/run.py b/examples/discrete_1d_2min/run.py
index fc7f31f..7a169d2 100644
--- a/examples/discrete_1d_2min/run.py
+++ b/examples/discrete_1d_2min/run.py
@@ -26,7 +26,7 @@
         observations.append(o[shift:][::lag])
 
     # initial HMM
-    hmm = bhmm.estimate_hmm(observations, nstates, output_model_type='discrete')
+    hmm = bhmm.estimate_hmm(observations, nstates, type='discrete')
     its[i] = lag*hmm.timescales
     likelihoods[i] = hmm.likelihood
 
diff --git a/examples/p5ab-hairpin/generate-figure.py b/examples/p5ab-hairpin/generate-figure.py
index 77857ea..78d047c 100644
--- a/examples/p5ab-hairpin/generate-figure.py
+++ b/examples/p5ab-hairpin/generate-figure.py
@@ -50,7 +50,7 @@ def run(nstates, nsamples):
 
     # Initialize BHMM, using MLHMM model as initial model.
     print "Initializing BHMM and running with "+str(nsamples)+" samples."
-    sampler = bhmm.BayesianHMMSampler(O, nstates, initial_model=mle)
+    sampler = bhmm.BHMM(O, nstates, initial_model=mle)
 
     # Sample models.
     bhmm_models = sampler.sample(nsamples=nsamples, save_hidden_state_trajectory=False)
diff --git a/examples/rnase-h-d10a/generate-figure.py b/examples/rnase-h-d10a/generate-figure.py
index c266708..33b20f9 100644
--- a/examples/rnase-h-d10a/generate-figure.py
+++ b/examples/rnase-h-d10a/generate-figure.py
@@ -46,7 +46,7 @@ def run(nstates, nsamples):
 
     # Initialize BHMM, using MLHMM model as initial model.
     print "Initializing BHMM and running with "+str(nsamples)+" samples."
-    sampler = bhmm.BayesianHMMSampler(O, nstates, initial_model=mle)
+    sampler = bhmm.BHMM(O, nstates, initial_model=mle)
 
     # Sample models.
     bhmm_models = sampler.sample(nsamples=nsamples, save_hidden_state_trajectory=False)
diff --git a/examples/synthetic-three-state-model/generate-figure.py b/examples/synthetic-three-state-model/generate-figure.py
index 21cd921..3b4fb59 100644
--- a/examples/synthetic-three-state-model/generate-figure.py
+++ b/examples/synthetic-three-state-model/generate-figure.py
@@ -61,7 +61,7 @@ def run(nstates, nsamples):
 
     # Initialize BHMM with MLHMM model.
     print "Sampling models from BHMM..."
-    sampler = bhmm.BayesianHMMSampler(O, nstates, initial_model=mle)
+    sampler = bhmm.BHMM(O, nstates, initial_model=mle)
     bhmm_models = sampler.sample(nsamples=nsamples, save_hidden_state_trajectory=False)
 
     # Generate a sample saving a hidden state trajectory.