Search results for "bayesian"
showing 10 items of 604 documents
Comparison of different uncertainty techniques in urban stormwater quantity and quality modelling
2011
Abstract Urban drainage models are important tools used by both practitioners and scientists in the field of stormwater management. These models are often conceptual and usually require calibration using local datasets. The quantification of the uncertainty associated with the models is a must, although it is rarely practiced. The International Working Group on Data and Models, which works under the IWA/IAHR Joint Committee on Urban Drainage, has been working on the development of a framework for defining and assessing uncertainties in the field of urban drainage modelling. A part of that work is the assessment and comparison of different techniques generally used in the uncertainty assessm…
Uncertainty estimation of a complex water quality model: The influence of Box–Cox transformation on Bayesian approaches and comparison with a non-Bay…
2012
Abstract In urban drainage modelling, uncertainty analysis is of undoubted necessity. However, uncertainty analysis in urban water-quality modelling is still in its infancy and only few studies have been carried out. Therefore, several methodological aspects still need to be experienced and clarified especially regarding water quality modelling. The use of the Bayesian approach for uncertainty analysis has been stimulated by its rigorous theoretical framework and by the possibility of evaluating the impact of new knowledge on the modelling predictions. Nevertheless, the Bayesian approach relies on some restrictive hypotheses that are not present in less formal methods like the Generalised L…
Variable Selection in Predictive MIDAS Models
2014
In short-term forecasting, it is essential to take into account all available information on the current state of the economic activity. Yet, the fact that various time series are sampled at different frequencies prevents an efficient use of available data. In this respect, the Mixed-Data Sampling (MIDAS) model has proved to outperform existing tools by combining data series of different frequencies. However, major issues remain regarding the choice of explanatory variables. The paper first addresses this point by developing MIDAS based dimension reduction techniques and by introducing two novel approaches based on either a method of penalized variable selection or Bayesian stochastic searc…
A Bayesian Network Model for Fire Assessment and Prediction
2015
Smartphones and other wearable computers with modern sensor technologies are becoming more advanced and widespread. This paper proposes exploiting those devices to help the firefighting operation. It introduces a Bayesian network model that infers the state of the fire and predicts its future development based on smartphone sensor data gathered within the fire area. The model provides a prediction accuracy of 84.79i¾?% and an area under the curve of 0.83. This solution had also been tested in the context of a fire drill and proved to help firefighters assess the fire situation and speed up their work.
A predictive maintenance policy with imperfect monitoring
2010
For many systems,failure is a very dangerous or costly event. To reduce the occurrence of this event,it is necessary to implement a preventive maintenance policy to replace the critical elements before failure.Since elements do not often exhibit incipient faults, they are replaced before a complete exploiting of their useful life.To conjugate the objective of exploiting elements for almost all their useful life with the objective to avoid failure,condition based and,more recently,predictive maintenance policies have been proposed.This paper deals with this topic and proposes a procedure for the computation of the maintenance time that minimizes the global maintenance cost.By adopting a stoc…
Uncertainty related to climate change in the assessment of the DDF curve parameters
2017
In the context of climate change, the evaluation of the parameters of Depth-Duration-Frequency (DDF) curves has become a critical issue. Neglecting future rainfall variations could result in an overestimation/underestimation of DDF parameters and, consequently, of the design storm. In this study, uncertainty analysis was integrated into trend analysis to provide an estimate of trends that cannot actually be rigorously verified. A Bayesian procedure was suggested for the updating of DDF curve parameters and to evaluate the uncertainty related to their assessment. The proposed procedure also allowed identification of the years of a series that contributed most to the overall uncertainty relat…
Bayesian inference analysis of the uncertainty linked to the evaluation of potential flood damage in urban areas.
2012
Flood damage in urbanized watersheds may be assessed by combining the flood depth–damage curves and the outputs of urban flood models. The complexity of the physical processes that must be simulated and the limited amount of data available for model calibration may lead to high uncertainty in the model results and consequently in damage estimation. Moreover depth–damage functions are usually affected by significant uncertainty related to the collected data and to the simplified structure of the regression law that is used. The present paper carries out the analysis of the uncertainty connected to the flood damage estimate obtained combining the use of hydraulic models and depth–damage curve…
Modelling spatially sampled proportion processes
2018
Many ecological processes are measured as proportions and are spatially sampled. In all these cases the standard procedure has long been the transformation of proportional data with the arcsine square root or logit transformation, without considering the spatial correlation in any way. This paper presents a robust regression model to analyse this kind of data using a beta regression and including a spatially correlated term within the Bayesian framework. As a practical example, we apply the proposed approach to a spatio-temporally sampled fishery discard dataset.
Editorial: Methodological issues in psychology and social sciences research
2023
In recent decades, the classical methods of studying the psychometric properties of tests have advanced to evolve into new innovative statistical methods, which allows obtaining a new view of the data. In this sense, progress has been made in the use of item response theory and in the use of structural equation models. Likewise, classical statistical inference methods have advanced in hypothesis testing and Bayesian statistics is rapidly making its way both in experimental studies and in psychometric studies. The aim of this issue has been to contribute to the dissemination of new research methodologies in quantitative and qualitative analysis in Psychology, as well as to evaluate the effec…
Bayesian two-stage regression with parametric heteroscedasticity
2008
In this paper, we expand Kleibergen and Zivot's (2003) Bayesian two-stage (B2S) model by allowing for unequal variances. Our choice for modeling heteroscedasticity is a fully Bayesian parametric approach. As an application, we present a cross-country Cobb–Douglas production function estimation.