Add to ORKGBayesian statistics may constitute the core of a consistent and comprehensive framework for the statistical aspects of modelling complex processes that involve many parameters whose values are derived from many sources. Bayesian statistics holds great promises for model calibration, provides the perfect starting point for uncertainty analysis and provides an excellent starting point for decision support. The purpose of this paper is to draw attention to problems and possible solutions. It is not our intention to introduce ready-for-use method
When we use simulation to estimate the performance of a stochastic system, the simulation often cont...
Model calibration is an essential test that dynamic hypotheses must pass in order to serve as tools ...
Decision-analytic models must often be informed using data that are only indirectly related to the m...
Bayesian statistics may constitute the core of a consistent and comprehensive framework for the stat...
Bayesian statistics may constitute the core of a consistent and comprehensive framework for the stat...
This book describes how Bayesian methods work. Its primary aim is to demystify them, and to show rea...
We consider prediction and uncertainty analysis for systems which are approximated using complex mat...
This thesis is concerned with the calibration of disease models in order to inform decisions about p...
In model development, model calibration and validation play complementary roles toward learning reli...
Abstract: When the goal is inference about an unknown θ and prediction of future data D * on the bas...
Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowled...
During the exploratory phase of a typical statistical analysis it is natural to look at the data in...
<p>Bayesian calibration is used to study computer models in the presence of both a calibration param...
We describe a Bayesian methodology for fitting deterministic dynamic models, demonstrating how this ...
A new Bayesian modeling framework is proposed to account for the uncertainty in the model parameters...
When we use simulation to estimate the performance of a stochastic system, the simulation often cont...
Model calibration is an essential test that dynamic hypotheses must pass in order to serve as tools ...
Decision-analytic models must often be informed using data that are only indirectly related to the m...
Bayesian statistics may constitute the core of a consistent and comprehensive framework for the stat...
Bayesian statistics may constitute the core of a consistent and comprehensive framework for the stat...
This book describes how Bayesian methods work. Its primary aim is to demystify them, and to show rea...
We consider prediction and uncertainty analysis for systems which are approximated using complex mat...
This thesis is concerned with the calibration of disease models in order to inform decisions about p...
In model development, model calibration and validation play complementary roles toward learning reli...
Abstract: When the goal is inference about an unknown θ and prediction of future data D * on the bas...
Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowled...
During the exploratory phase of a typical statistical analysis it is natural to look at the data in...
<p>Bayesian calibration is used to study computer models in the presence of both a calibration param...
We describe a Bayesian methodology for fitting deterministic dynamic models, demonstrating how this ...
A new Bayesian modeling framework is proposed to account for the uncertainty in the model parameters...
When we use simulation to estimate the performance of a stochastic system, the simulation often cont...
Model calibration is an essential test that dynamic hypotheses must pass in order to serve as tools ...
Decision-analytic models must often be informed using data that are only indirectly related to the m...