diff --git a/docs/api.rst b/docs/api.rst
index 6a16787d0..58633f043 100644
--- a/docs/api.rst
+++ b/docs/api.rst
@@ -163,14 +163,14 @@ Sparse Frameworks
 Abstract Sparse Objects
 *********************************
 
-.. autoclass:: VariationalPosterior
+.. autoclass:: AbstractVariationalInference
    :members:
 
 
 Sparse Methods
 *********************************
 
-.. autoclass:: SVGP
+.. autoclass:: StochasticVI
    :members:
 
 
diff --git a/docs/nbs/sparse_regression.pct.py b/docs/nbs/sparse_regression.pct.py
index 1eec88617..f933eed6d 100644
--- a/docs/nbs/sparse_regression.pct.py
+++ b/docs/nbs/sparse_regression.pct.py
@@ -124,10 +124,10 @@
 # Here, the variational process $q(\cdot)$ depends on the prior through $p(f(\cdot)|f(\boldsymbol{z}))$ in $(\times)$.
 # %% [markdown]
 # 
-# We combine our true and approximate posterior Gaussian processes into an `SVGP` object to define the variational strategy that we will adopt in the forthcoming inference.
+# We combine our true and approximate posterior Gaussian processes into an `StochasticVI` object to define the variational strategy that we will adopt in the forthcoming inference.
 
 # %%
-svgp = gpx.SVGP(posterior=p, variational_family=q)
+svgp = gpx.StochasticVI(posterior=p, variational_family=q)
 
 # %% [markdown]
 # ## Inference
diff --git a/gpjax/__init__.py b/gpjax/__init__.py
index a407ad8e6..53136e8b4 100644
--- a/gpjax/__init__.py
+++ b/gpjax/__init__.py
@@ -20,7 +20,7 @@
 from .likelihoods import Bernoulli, Gaussian
 from .mean_functions import Constant, Zero
 from .parameters import copy_dict_structure, initialise, transform
-from .sparse_gps import SVGP
+from .sparse_gps import StochasticVI
 from .types import Dataset
 from .variational import VariationalGaussian, WhitenedVariationalGaussian
 
diff --git a/gpjax/sparse_gps.py b/gpjax/sparse_gps.py
index 4c37e87e1..2a52d9b9d 100644
--- a/gpjax/sparse_gps.py
+++ b/gpjax/sparse_gps.py
@@ -14,8 +14,8 @@
 
 
 @dataclass
-class VariationalPosterior:
-    """A variational posterior object. With reference to some true posterior distribution :math:`p`, this can be used to minimise the KL-divergence between :math:`p` and a variational posterior :math:`q`."""
+class AbstractVariationalInference:
+    """A base class for inference and training of variational families against an extact posterior"""
 
     posterior: AbstractPosterior
     variational_family: VariationalFamily
@@ -50,8 +50,8 @@ def elbo(
 
 
 @dataclass
-class SVGP(VariationalPosterior):
-    """Sparse Variational Gaussian Process (SVGP) training module. The key reference is Hensman et. al., (2013) - Gaussian processes for big data."""
+class StochasticVI(AbstractVariationalInference):
+    """Stochastic Variational inference training module. The key reference is Hensman et. al., (2013) - Gaussian processes for big data."""
 
     def __post_init__(self):
         self.prior = self.posterior.prior
diff --git a/tests/test_abstractions.py b/tests/test_abstractions.py
index 155b2377d..5c21f2598 100644
--- a/tests/test_abstractions.py
+++ b/tests/test_abstractions.py
@@ -51,7 +51,7 @@ def test_batch_fitting(nb, ndata):
 
     q = gpx.VariationalGaussian(prior=prior, inducing_inputs=z)
 
-    svgp = gpx.SVGP(posterior=p, variational_family=q)
+    svgp = gpx.StochasticVI(posterior=p, variational_family=q)
     params, trainable_status, constrainer, unconstrainer = initialise(svgp)
     params = gpx.transform(params, unconstrainer)
     objective = svgp.elbo(D, constrainer)
diff --git a/tests/test_sparse_gps.py b/tests/test_sparse_gps.py
index 4d2277f15..b7ebf56d2 100644
--- a/tests/test_sparse_gps.py
+++ b/tests/test_sparse_gps.py
@@ -39,7 +39,7 @@ def test_svgp(n_datapoints, n_inducing_points, n_test, whiten, diag, jit_fns):
         inducing_inputs=inducing_inputs, diag=diag
         )
 
-    svgp = gpx.SVGP(posterior=post, variational_family=q)
+    svgp = gpx.StochasticVI(posterior=post, variational_family=q)
 
     assert svgp.posterior.prior == post.prior
     assert svgp.posterior.likelihood == post.likelihood