activity
20172023
most citedNeural network model for imprecise regression with interval dependent variables

20 citations · 30 across the 7 of their papers we have counts for

collaborators

8 papers

stat.OT2023

Correlation-Based And-Operations Can Be Copulas: A Proof

Enrique Miralles-Dolz, Ander Gray, Edoardo Patelli +3

In many practical situations, we know the probabilities and of two events and , and we want to estimate the joint probability . The algorithm t…

stat.ME2022★ 6 cited

Should data ever be thrown away? Pooling interval-censored data sets with different precision

Krasymyr Tretiak, Scott Ferson

Data quality is an important consideration in many engineering applications and projects. Data collection procedures do not always involve careful utilization of the most precise i…

math.PR2022★ 3 cited

Correlated Boolean Operators for Uncertainty Logic

Enrique Miralles-Dolz, Ander Gray, Edoardo Patelli +1

We present a correlated \textit{and} gate which may be used to propagate uncertainty and dependence through Boolean functions, since any Boolean function may be expressed as a comb…

physics.data-an2022★ 20 cited

Neural network model for imprecise regression with interval dependent variables

Krasymyr Tretiak, Georg Schollmeyer, Scott Ferson

This paper presents a computationally feasible method to compute rigorous bounds on the interval-generalisation of regression analysis to account for epistemic uncertainty in the o…

cs.MS2021

The Creation of Puffin, the Automatic Uncertainty Compiler

Nicholas Gray, Marco De Angelis, Scott Ferson

An uncertainty compiler is a tool that automatically translates original computer source code lacking explicit uncertainty analysis into code containing appropriate uncertainty rep…

stat.ME2021★ 1 cited

Singhing with Confidence: Visualising the Performance of Confidence Structures

Alexander Wimbush, Nicholas Gray, Scott Ferson

Confidence intervals are an established means of portraying uncertainty about an inferred parameter and can be generated through the use of confidence distributions. For a confiden…