  We study the problem of estimating the diagonal of an implicitly given matrix
$A$. For such a matrix we have access to an oracle that allows us to evaluate
the matrix vector product $Av$. For random variable $v$ drawn from an
appropriate distribution, this may be used to return an estimate of the
diagonal of the matrix $A$. Whilst results exist for probabilistic guarantees
relating to the error of estimates of the trace of $A$, no such results have
yet been derived for the diagonal. We analyse the number of queries $s$
required to guarantee that with probability at least $1-\delta$ the estimates
of the relative error of the diagonal entries is at most $\varepsilon$. We
extend this analysis to the 2-norm of the difference between the estimate and
the diagonal of $A$. We prove, discuss and experiment with bounds on the number
of queries $s$ required to guarantee a probabilistic bound on the estimates of
the diagonal by employing Rademacher and Gaussian random variables. Two
sufficient upper bounds on the minimum number of query vectors are proved,
extending the work of Avron and Toledo [JACM 58(2)8, 2011], and later work of
Roosta-Khorasani and Ascher [FoCM 15, 1187-1212, 2015]. We find that,
generally, there is little difference between the two, with convergence going
as $O(\log(1/\delta)/\varepsilon^2)$ for individual diagonal elements. However
for small $s$, we find that the Rademacher estimator is superior. These results
allow us to then extend the ideas of Meyer, Musco, Musco and Woodruff [SOSA,
142-155, 2021], suggesting algorithm Diag++, to speed up the convergence of
diagonal estimation from $O(1/\varepsilon^2)$ to $O(1/\varepsilon)$ and make it
robust to the spectrum of any positive semi-definite matrix $A$.
