diff --git a/README.md b/README.md
index b2e65d7..d1ce50f 100644
--- a/README.md
+++ b/README.md
@@ -35,15 +35,20 @@ For collecting the SWAG parameters, two possible schedules are supported:
 * After the end of each training epoch (default, `collect_steps = 0` for `SwagUpdateCallback`)
 * After each N training steps (set `collect_steps > 0` for `SwagUpdateCallback`)
 
-### SWA versus SWAG
+### SWA, SWAG-Diagonal, and SWAG
 
-The library supports both SWA (stochastic weight averaging without
-covariance estimation) and SWAG (stochastic weight averaging with
-Gaussian covariance estimation). The method is selected by the
-`no_cov_mat` attribute when initializing the model
-(e.g. `SwagModel.from_base(model, no_cov_mat=True)`). The default
-value `True` corresponds to SWA, and you need to explicitly set
-`no_cov_mat=False` to activate SWAG.
+The library supports both SWA (stochastic weight averaging) and two
+variants of SWAG (SWA-Gaussian): SWAG-Diagonal that uses diagonal
+covariance and "full" SWAG that does low-rank covariance matrix
+estimation.
+
+The method is selected by the `no_cov_mat` attribute when initializing
+the model (e.g. `SwagModel.from_base(model, no_cov_mat=True)`). The
+default value `True` works only with SWAG-Diagonal and SWA, and you
+need to explicitly set `no_cov_mat=False` to activate the low-rank
+covariance estimation of SWAG. Note that you can also test SWA and
+SWAG-Diagonal methods when the model is trained with
+`no_cov_mat=False` (see the next section).
 
 With SWAG, the `max_num_models` option controls the maximum rank of
 the covariance matrix. The rank is increased by each parameter
@@ -66,6 +71,16 @@ class provides the convenience method `get_logits()` that samples the
 parameters and makes a new prediction `num_predictions` times, and
 returns the logit values in a tensor.
 
+Note that both for `sample_parameters()` and `get_logits()` the
+default keyword arguments are suitable only for SWAG-Diagonal. For
+SWAG, you should use `cov=True` (required to use the covariance
+matrix) and `scale=0.5` (recommended). For SWA, you should use
+`cov=False` and `scale=0`. To summarize:
+
+* SWA: `scale=0`, `cov=False`
+* SWAG-Diagonal: `scale=1`, `cov=False` (defaults)
+* SWAG: `scale=0.5`, `cov=True` (`no_cov_mat=False` required for the model)
+
 ### Currently supported models
 
 * BERT (bidirectional encoder)