diff --git a/paper/imputation.tex b/paper/imputation.tex
index 5ea43b965677b7bcd5d4de56d902cd2f1f913c85..6306de302fbc00152de8c8de5cb0714964c1cc45 100644
--- a/paper/imputation.tex
+++ b/paper/imputation.tex
@@ -205,7 +205,7 @@ Note that we are making the simplifying assumption that coefficients $\gamma$ ar
 %\spara{Parameter estimation} 
 We take a Bayesian approach to learn the model over the dataset \dataset.
 %
-In particular, we consider the full probabilistic model defined in Equations \ref{eq:judgemodel} -- \ref{eq:defendantmodel} and obtain the posterior distribution of its parameters $\parameters = \{ \alpha_\outcomeValue, \beta_\obsFeaturesValue, \beta_\unobservableValue,  \gamma_\obsFeaturesValue, \gamma_\unobservableValue\}$. % and $\alpha$ for all $\human$. %, where $i = 1, \ldots, \datasize$, conditional on the dataset.
+In particular, we consider the full probabilistic model defined in Equations \ref{eq:judgemodel} -- \ref{eq:defendantmodel} and obtain the posterior distribution of its parameters $\parameters = \{ \alpha_\outcomeValue, \beta_\obsFeaturesValue, \beta_\unobservableValue,  \gamma_\obsFeaturesValue, \gamma_\unobservableValue\} \cup\{\alpha_\human\}_\human$, which includes intercepts $\alpha_\human$ for all $\human$ employed in the data. % and $\alpha$ for all $\human$. %, where $i = 1, \ldots, \datasize$, conditional on the dataset.
 % 
 %Notice that by ``parameters'' here we refer to all quantities that are not considered as known with certainty from the input, and so parameters include unobserved features \unobservable.
 %