@@ -205,7 +205,7 @@ Note that we are making the simplifying assumption that coefficients $\gamma$ ar
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@@ -205,7 +205,7 @@ Note that we are making the simplifying assumption that coefficients $\gamma$ ar
%\spara{Parameter estimation}
%\spara{Parameter estimation}
We take a Bayesian approach to learn the model over the dataset \dataset.
We take a Bayesian approach to learn the model over the dataset \dataset.
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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.
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%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.
%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.
