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It has been previously shown that any measurement system specific relationship (SSR)/ mathematical-model Y_d = f_d ({X_m}) or so is bracketed with certain parameters which should prefix the achievable-accuracy/ uncertainty (e_d^Y) of a desired result y_d. Here we clarify how the element-specific-expressions of isotopic abundances and/ or atomic weight could be parametrically distinguished from one another, and the achievable accuracy be even a priori predicted. It is thus signified that, irrespective of whether the measurement-uncertainty (u_m) could be purely random by origin or not, e_d^Y should be a systematic parameter. Further, by property-governing-factors, any SSR should belong to either variable-independent (F.1) or -dependent (F.2) family of SSRs/ models. The SSRs here are shown to be the members of the F.2 family. That is, it is pointed out that, and explained why, the uncertainty (e) of determining an either isotopic abundance or atomic weight should vary, even for any given measurement-accuracy(s) u_m(s), as a function of the measurable-variable(s) X_m(s). However, the required computational-step has been shown to behave as an error-sink in the overall process of indirect measurement in question.
Any physiochemical variable (Ym) is always determined from certain measured variables {Xi}. The uncertainties {ui} of measuring {Xi} are generally a priori ensured as acceptable. However, there is no general method for assessing uncertainty (em) in t
Previously, we presented a new interpretation of quantum mechanics that revealed it is indeed possible to have a local hidden variable that is consistent with Bells inequality experiments. In that article we suggested that the local hidden variable i
When the cooling rate $v$ is smaller than a certain material-dependent threshold, the glass transition temperature $T_g$ becomes to a certain degree the material parameter being nearly independent on the cooling rate. The common method to determine $
We consider the core reinforcement-learning problem of on-policy value function approximation from a batch of trajectory data, and focus on various issues of Temporal Difference (TD) learning and Monte Carlo (MC) policy evaluation. The two methods ar
To interpret uncertainty estimates from differentiable probabilistic models, recent work has proposed generating Counterfactual Latent Uncertainty Explanations (CLUEs). However, for a single input, such approaches could output a variety of explanatio