\begin{abstract}

\vspace{-.15in}
   
We present the first systematic study of effectiveness of \robts on a diverse
set of \totalprogs{} probabilistic programs representing generalized linear
models, mixture models, and time-series models. We evaluate five \robts from
literature on each model. We quantify and present insights on (1)~the
improvement of the posterior prediction accuracy and (2)~the execution time
overhead of the robustified programs, in the presence of three input noise
models.

To automate the evaluation of various \robts, we developed \mbox{\NAME{}} --
a novel framework for quantifying the robustness of probabilistic programs and
exploring the trade-offs between robustness and execution time.
Our experimental results indicate that the existing \trans are often suitable
 only for specific noise models, can significantly increase execution time, and
 have non-trivial interaction with \mbox{the inference algorithms.}
    


  
\end{abstract}
