mathmarks

Marks out of 100 for 88 students taking examinations in mechanics (C), vectors (C), algebra (0), analysis (O) and statistics (O), where C indicates closed and O indicates open book examination.

datasets

An implementation of sensitivity and robustness methods in Bayesian networks in R. It includes methods to perform parameter variations via a variety of co-variation schemes, to compute sensitivity functions and to quantify the dissimilarity of two Bayesian networks via distances and divergences. It further includes diagnostic methods to assess the goodness of fit of a Bayesian networks to data, including global, node and parent-child monitors. Reference: M. Leonelli, R. Ramanathan, R.L. Wilkerson (2022) <doi:10.1016/j.knosys.2023.110882>.

Manuele Leonelli

bnmonitor

An Implementation of Sensitivity Analysis in Bayesian Networks

Ramsiya Ramanathan

Rachel Wilkerson

mathmarks function

A dataframe with 88 observations on the following 5 variables<ul>
<li>mechanics: mark out of 100 for mechanics</li>
<li>vectors: mark out of 100 for vectors</li>
<li>algebra: mark out of 100 for algebra</li>
<li>analysis: mark out of 100 for analysis</li>
<li>statistics: mark out of 100 for statistics</li>
</ul>

mathmarks: Math Marks Data

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