Add to ORKGQuantifying uncertainty for predictions with model error in non-Gaussian systems with intermittency This article has been downloaded from IOPscience. Please scroll down to see the full text article
Uncertainty analysis is an important part of system design. The formula for error propagation throug...
Computational models for large systems are sometimes built in a hierarchical way from simple compone...
This paper presents a method for prediction of uncertain closed loop systems, where the uncertaintie...
How do we know how much we know? Quantifying uncertainty associated with our modelling work is the o...
The field of uncertainty quantification is evolving rapidly because of increasing emphasis on models...
Biological processes are often modelled using ordinary differential equations. The unknown parameter...
There is always a deviation between a model prediction and the reality that the model intends to rep...
Uncertainty quantification may appear daunting for practitioners due to its inherent complexity but ...
The starting point in uncertainty quantification is a stochastic model, which is fitted to a technic...
7 pages, 4 figures, 2 tablesThe parameters of dynamical models of biological processes always posses...
Machine learning and artificial intelligence will be deeply embedded in the intelligent systems huma...
Multi-parameter models in systems biology are typically ‘sloppy’: some parameters or combinations of...
The probability distribution of a model prediction is presented as a proper basis for evaluating the...
In this paper we focus on the problem of assigning uncertainties to single-point predictions generat...
Predictive accuracy is the sum of two kinds of uncertainty–natural variability and modeling uncertai...
Uncertainty analysis is an important part of system design. The formula for error propagation throug...
Computational models for large systems are sometimes built in a hierarchical way from simple compone...
This paper presents a method for prediction of uncertain closed loop systems, where the uncertaintie...
How do we know how much we know? Quantifying uncertainty associated with our modelling work is the o...
The field of uncertainty quantification is evolving rapidly because of increasing emphasis on models...
Biological processes are often modelled using ordinary differential equations. The unknown parameter...
There is always a deviation between a model prediction and the reality that the model intends to rep...
Uncertainty quantification may appear daunting for practitioners due to its inherent complexity but ...
The starting point in uncertainty quantification is a stochastic model, which is fitted to a technic...
7 pages, 4 figures, 2 tablesThe parameters of dynamical models of biological processes always posses...
Machine learning and artificial intelligence will be deeply embedded in the intelligent systems huma...
Multi-parameter models in systems biology are typically ‘sloppy’: some parameters or combinations of...
The probability distribution of a model prediction is presented as a proper basis for evaluating the...
In this paper we focus on the problem of assigning uncertainties to single-point predictions generat...
Predictive accuracy is the sum of two kinds of uncertainty–natural variability and modeling uncertai...
Uncertainty analysis is an important part of system design. The formula for error propagation throug...
Computational models for large systems are sometimes built in a hierarchical way from simple compone...
This paper presents a method for prediction of uncertain closed loop systems, where the uncertaintie...