The authors are developing a computer application, called the Bayes Inference Engine, to provide the means to make inferences about models of physical reality within a Bayesian framework. The construction of complex nonlinear models is achieved by a fully object-oriented design. The models are represented by a data-flow diagram that may be manipulated by the analyst through a graphical programming environment. Maximum a posteriori solutions are achieved using a general, gradient-based optimization algorithm. The application incorporates a new technique of estimating and visualizing the uncertainties in specific aspects of the model
In a range of different scientific fields, deterministic calculations for which there is no analytic...
This paper advocates expansion of the role of Bayesian statistical inference when formally quantifyi...
Although computer models are often used for forecasting future outcomes of complex systems, the unce...
The authors have developed a computer application, called the Bayes Inference Engine, to enable one ...
Abstract. We have developed a computer application, called the Bayes Inference Engine, to enable us ...
The Bayes Inference Engine (BIE) is a flexible software tool that allows one to interactively define...
The Bayes Inference Engine (BIE) is a flexible software tool that allows one to interactively define...
Differential equation models are used in a wide variety of scientific fields to describe the behavio...
Part 2: UQ TheoryInternational audienceMost large and complex physical systems are studied by mathem...
International audienceHow to use an incomplete and uncertain model of the environment to perceive, i...
Test and analysis plays a vital role in reducing uncertainty about the true performance of an engine...
In many areas of science, models are used to describe attributes of complex systems. These models ar...
We harness the power of Bayesian emulation techniques, designed to aid the analysis of complex compu...
Although probabilistic modeling and Bayesian inference provide a unifying theoretical framework for ...
We often want to learn about physical processes that are described by complex nonlinear mathematical...
In a range of different scientific fields, deterministic calculations for which there is no analytic...
This paper advocates expansion of the role of Bayesian statistical inference when formally quantifyi...
Although computer models are often used for forecasting future outcomes of complex systems, the unce...
The authors have developed a computer application, called the Bayes Inference Engine, to enable one ...
Abstract. We have developed a computer application, called the Bayes Inference Engine, to enable us ...
The Bayes Inference Engine (BIE) is a flexible software tool that allows one to interactively define...
The Bayes Inference Engine (BIE) is a flexible software tool that allows one to interactively define...
Differential equation models are used in a wide variety of scientific fields to describe the behavio...
Part 2: UQ TheoryInternational audienceMost large and complex physical systems are studied by mathem...
International audienceHow to use an incomplete and uncertain model of the environment to perceive, i...
Test and analysis plays a vital role in reducing uncertainty about the true performance of an engine...
In many areas of science, models are used to describe attributes of complex systems. These models ar...
We harness the power of Bayesian emulation techniques, designed to aid the analysis of complex compu...
Although probabilistic modeling and Bayesian inference provide a unifying theoretical framework for ...
We often want to learn about physical processes that are described by complex nonlinear mathematical...
In a range of different scientific fields, deterministic calculations for which there is no analytic...
This paper advocates expansion of the role of Bayesian statistical inference when formally quantifyi...
Although computer models are often used for forecasting future outcomes of complex systems, the unce...