From 156ae9d50dc8f28b5e29c4c3072946edb8691f70 Mon Sep 17 00:00:00 2001
From: Antti Hyttinen <ajhyttin@gmail.com>
Date: Fri, 17 Jan 2020 09:06:58 +0200
Subject: [PATCH] WHat the hell is going on with the leniency....
---
paper/imputation.tex | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/paper/imputation.tex b/paper/imputation.tex
index 5ea43b9..6306de3 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.
%
--
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