coming from a binomial distribution with parameter $p$.](varpn1000m10)

We next turned to a normal distribution of marks. This permits us to study the impact of increasing the standard deviation in marks. With the previous binomial distribution of marks, the standard deviation is $\sqrt{100p(1-p)}$ which is fixed by $p$. In Figure 2, we plot the error in the predicted mark for varying standard deviations. The mean grade is fixed at 70 marks out of 100. We again see that the generalised PeerRank method outperforms simply averaging peer grades except when there is a very large standard deviation in marks.

![Performance of the generalised PeerRank method on marks from a normal distribution with a mean of 70 as we vary the standard deviation.](varnormn1000m10mean70)

Finally, we consider a simple uniform distribution of marks. We suppose that every mark from $lo$ to 100 is equally likely.

![Performance of the generalised PeerRank method on marks coming from the uniform distribution $[lo,100]$ for varying lower bound $lo$. ](varunilon1000m10)

In Figure 3, we plot the error in the predicted grade whilst we vary $lo$, the lowest possible mark. From $lo>20$, the generalised PeerRank method outperforms simply averaging the peer grades. For $lo \geq 50$, the error is 