Update docs/src/emulate.md

Co-authored-by: Kevin Phan  <98072684+ph-kev@users.noreply.github.com>

Author: odunbar

docs/src/emulate.md

@@ -13,7 +13,7 @@ input_output_pairs = Utilities.get_training_points(ekpobj, 5) # use first 5 iter
 ```
 
 !!! note "Minibatched calibration"
-    When the `EnsembleKalmanProcess` is built with an `ObservationSeries` and a minibatcher, each iteration uses a batch ``B_1, \ldots, B_n`` (``n \leq N``): a disjoint partition of statistically similar observations ``y_1, \ldots, y_N \sim \rho``. The batching can aid the calibration stage but does not effect the emulate-sample stages.
+    When the `EnsembleKalmanProcess` is built with an `ObservationSeries` and a minibatcher, each iteration uses a batch ``B_1, \ldots, B_n`` (``n \leq N``): a disjoint partition of statistically similar observations ``y_1, \ldots, y_N \sim \rho``. The batching can aid the calibration stage but does not affect the emulate-sample stages.
 
     Internally `get_training_points` and `encoder_kwargs_from` reduce outputs/noise from the `EnsembleKalmanProcess` to thei per-observation components, thus the emulator is trained on ``(\theta, \mathcal{G}(\theta))`` pairs representative of the distribution ``\rho``. In the Sampling stage the full observation set ``y_1, \ldots, y_N`` is used and batch structure is ignored.