With financial modelling requiring a better understanding of model risk, it is helpful to be able to vary assumptions about underlying probability distributions in an efficient manner, preferably without the noise induced by resampling distributions managed by Monte Carlo methods. This paper presents differential equations and solution methods for the functions of the form Q(x) = F−1(G(x)), where F and G are cumulative distribution functions. Such functions allow the direct recycling of Monte Carlo samples from one distribution into samples from another. The method may be developed analytically for certain special cases, and illuminate the idea that it is a more precise form of the traditional Cornish–Fisher expansion. In this manner the model risk of distributional risk may be assessed free of the Monte Carlo noise associated with resampling. The method may also be regarded as providing both analytical and numerical bases for doing more precise Cornish–Fisher transformations. Examples are given of equations for converting normal samples to Student t, and converting exponential to normal. In the case of the normal distribution, the change of variables employed allows the sampling to take place to good accuracy based on a single rational approximation over a very wide range of sample space. The avoidance of branching statements is of use in optimal graphics processing unit (GPU) computations as it avoids the effect of branch divergence. We give a branch-free normal quantile that offers performance improvements in a GPU environment while retaining the best precision characteristics of well-known methods. We also offer models with low probability branch divergence. Comparisons of new and existing forms are made on Nvidia GeForce GTX Titan and Tesla C2050 GPUs. We argue that in both single- and double-precisions, the change-of-variables approach offers the most GPU-optimal Gaussian quantile yet, working faster than the Cuda 5.5 built-in function.

The author contends that Leonardo Sciascia's L'affaire Moro is not a work of non-fiction, as Sciascia proposed, but of historical fiction, and that Sciascia's Moro is a literary character, more a spokesperson for Sciascia's political views than a reflection of the historical figure. Sciascia's Moro embodies the same qualities as many of Sciascia's other protagonists, such as a radical individualism and willingness to sacrifice all in order to protect their dignity and liberty. What emanates from the text is a ‘postmodern’ blend that interprets and imposes a narrative hierarchy on events, and conveys a mental reality that need not necessarily coincide with what can be proven with evidence. In fact, Sciascia combines factual information and his own ‘conjectural knowledge’ to convince his reader of the ‘moral truth’ of his argument. Sciascia's is indeed a strong narrative in that it succeeded in shaping how the Italian public views to this day a critical juncture in its recent history.