The dynamics of systems have proven to be very powerful tools in understanding the behavior of different natural phenomena throughout the last two centuries. However, the attributes of natural systems are observed to deviate from their classical states due to the effect of different types of uncertainties. Actually, randomness and impreciseness are the two major sources of uncertainties in natural systems. Randomness is modeled by different stochastic processes and impreciseness could be modeled by fuzzy sets, rough sets, Dempster–Shafer theory, etc
This paper was presented at the 3rd Micro and Nano Flows Conference (MNF2011), which was held at the...
Preferences that accommodate aversion to subjective uncertainty and its potential misspecification i...
This paper deals with uncertain dynamical systems in which predictions about the future state of a s...
We consider time-average quantities of chaotic systems and their sensitivity to system parameters. W...
Simulation models are increasingly used for exploring the consequences of deep uncertainty in comple...
Introductory Statistics and Random Phenomena integrates traditional statistical data analysis with n...
In this paper we present and illustrate basic Bayesian techniques for the uncertainty analysis of co...
AbstractThe aim of this work is to develop quantitative approaches to manage uncertainty of variable...
We explore decision-making under uncertainty using a framework that decomposes uncertainty into thre...
Predictive accuracy is the sum of two kinds of uncertainty–natural variability and modeling uncertai...
Computer simulation of real world phenomena is now ubiquitous in science, because experimentation in...
This three-fold contribution to the field covers both theory and current research in algorithmic app...
The better the model, the more features of the problem it explains. However, showing that the model ...
This thesis develops theoretical and computational methods for the robustness analysis of uncertain ...
International audienceThis book results from a course developed by the author and reflects both his ...
This paper was presented at the 3rd Micro and Nano Flows Conference (MNF2011), which was held at the...
Preferences that accommodate aversion to subjective uncertainty and its potential misspecification i...
This paper deals with uncertain dynamical systems in which predictions about the future state of a s...
We consider time-average quantities of chaotic systems and their sensitivity to system parameters. W...
Simulation models are increasingly used for exploring the consequences of deep uncertainty in comple...
Introductory Statistics and Random Phenomena integrates traditional statistical data analysis with n...
In this paper we present and illustrate basic Bayesian techniques for the uncertainty analysis of co...
AbstractThe aim of this work is to develop quantitative approaches to manage uncertainty of variable...
We explore decision-making under uncertainty using a framework that decomposes uncertainty into thre...
Predictive accuracy is the sum of two kinds of uncertainty–natural variability and modeling uncertai...
Computer simulation of real world phenomena is now ubiquitous in science, because experimentation in...
This three-fold contribution to the field covers both theory and current research in algorithmic app...
The better the model, the more features of the problem it explains. However, showing that the model ...
This thesis develops theoretical and computational methods for the robustness analysis of uncertain ...
International audienceThis book results from a course developed by the author and reflects both his ...
This paper was presented at the 3rd Micro and Nano Flows Conference (MNF2011), which was held at the...
Preferences that accommodate aversion to subjective uncertainty and its potential misspecification i...
This paper deals with uncertain dynamical systems in which predictions about the future state of a s...