Search results for "Bayesian [statistical analysis]"
showing 10 items of 299 documents
A Bayesian network model for evacuation time analysis during a ship fire
2013
We present an evacuation model for ships while a fire happens onboard. The model is designed by utilizing Bayesian networks (BN) and then simulated in GeNIe software. In our proposed model, the most important factors that have significant influence on a rescue process and evacuation time are identified and analyzed. By applying the probability distribution of the considered factors collected from the literature including IMO, real empirical data and practical experiences, the trend of the rescue process and evacuation time can be evaluated and predicted using the proposed model. The results of this paper help understanding about possible consequences of influential factors on the security o…
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…
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…
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…
Enhancing TIR Image Resolution via Bayesian Smoothing for IRRISAT Irrigation Management Project
2013
Accurate estimation of physical quantities depends on the availability of High Resolution (HR) observations of the Earth surface. However, due to the unavoidable tradeoff between spatial and time resolution, the acquisition instants of HR data hardly coincides with those required by the estimation algorithms. A possible solution consists in constructing a synthetic HR observation at a given time k by exploiting Low Resolution (LR) and HR data acquired at different instants. In this work we recast this issue as a smoothing problem, thus focusing on cases in which observations acquired both before and after time k are available. The proposed approach is validated on a region of interest for t…
On the use of approximate Bayesian computation Markov chain Monte Carlo with inflated tolerance and post-correction
2020
Approximate Bayesian computation allows for inference of complicated probabilistic models with intractable likelihoods using model simulations. The Markov chain Monte Carlo implementation of approximate Bayesian computation is often sensitive to the tolerance parameter: low tolerance leads to poor mixing and large tolerance entails excess bias. We consider an approach using a relatively large tolerance for the Markov chain Monte Carlo sampler to ensure its sufficient mixing, and post-processing the output leading to estimators for a range of finer tolerances. We introduce an approximate confidence interval for the related post-corrected estimators, and propose an adaptive approximate Bayesi…
Deep Importance Sampling based on Regression for Model Inversion and Emulation
2021
Understanding systems by forward and inverse modeling is a recurrent topic of research in many domains of science and engineering. In this context, Monte Carlo methods have been widely used as powerful tools for numerical inference and optimization. They require the choice of a suitable proposal density that is crucial for their performance. For this reason, several adaptive importance sampling (AIS) schemes have been proposed in the literature. We here present an AIS framework called Regression-based Adaptive Deep Importance Sampling (RADIS). In RADIS, the key idea is the adaptive construction via regression of a non-parametric proposal density (i.e., an emulator), which mimics the posteri…
Compressed Particle Methods for Expensive Models With Application in Astronomy and Remote Sensing
2021
In many inference problems, the evaluation of complex and costly models is often required. In this context, Bayesian methods have become very popular in several fields over the last years, in order to obtain parameter inversion, model selection or uncertainty quantification. Bayesian inference requires the approximation of complicated integrals involving (often costly) posterior distributions. Generally, this approximation is obtained by means of Monte Carlo (MC) methods. In order to reduce the computational cost of the corresponding technique, surrogate models (also called emulators) are often employed. Another alternative approach is the so-called Approximate Bayesian Computation (ABC) sc…