Search results for "Bayesian inference"
showing 10 items of 120 documents
Does social capital matter for European regional growth?
2015
Abstract This paper analyzes the role of different elements of social capital in economic growth for a sample of 85 European regions during the period 1995–2008. Despite the remarkable progress that social capital and European regional economic growth literatures have experienced over the last two decades, initiatives combining the two are few, and entirely yet to come for the post-1990s period. Recent improvements in data availability allow this gap in the literature to be closed, since they enable the researcher to consider the traditionally disregarded Eastern and Central European (ECE) regions. This is particularly interesting, as they are all transition economies that recently joined t…
Japan's FDI drivers in a time of financial uncertainty. New evidence based on Bayesian Model Averaging
2021
En este artículo analizamos los determinantes del stock de FDI saliente de Japón para el período 1996–2017. Este período es especialmente relevante ya que abarca un proceso de creciente globalización económica y dos crisis financieras. Para ello, consideramos un amplio conjunto de variables candidatas basadas en la teoría, así como en análisis empíricos previos. Nuestra muestra incluye un total de 27 países anfitriones. Seleccionamos las covariables utilizando una metodología basada en datos, el análisis Bayesian Model Averaging (BMA). Además, también analizamos si estos determinantes cambian según el grado de desarrollo (emergentes vs desarrollados) o las áreas geográficas (UE vs Asia Orie…
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…
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…
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…
A Review of Multiple Try MCMC algorithms for Signal Processing
2018
Many applications in signal processing require the estimation of some parameters of interest given a set of observed data. More specifically, Bayesian inference needs the computation of {\it a-posteriori} estimators which are often expressed as complicated multi-dimensional integrals. Unfortunately, analytical expressions for these estimators cannot be found in most real-world applications, and Monte Carlo methods are the only feasible approach. A very powerful class of Monte Carlo techniques is formed by the Markov Chain Monte Carlo (MCMC) algorithms. They generate a Markov chain such that its stationary distribution coincides with the target posterior density. In this work, we perform a t…
Adaptive independent sticky MCMC algorithms
2018
In this work, we introduce a novel class of adaptive Monte Carlo methods, called adaptive independent sticky MCMC algorithms, for efficient sampling from a generic target probability density function (pdf). The new class of algorithms employs adaptive non-parametric proposal densities which become closer and closer to the target as the number of iterations increases. The proposal pdf is built using interpolation procedures based on a set of support points which is constructed iteratively based on previously drawn samples. The algorithm's efficiency is ensured by a test that controls the evolution of the set of support points. This extra stage controls the computational cost and the converge…
A Bayesian Multilevel Random-Effects Model for Estimating Noise in Image Sensors
2020
Sensor noise sources cause differences in the signal recorded across pixels in a single image and across multiple images. This paper presents a Bayesian approach to decomposing and characterizing the sensor noise sources involved in imaging with digital cameras. A Bayesian probabilistic model based on the (theoretical) model for noise sources in image sensing is fitted to a set of a time-series of images with different reflectance and wavelengths under controlled lighting conditions. The image sensing model is a complex model, with several interacting components dependent on reflectance and wavelength. The properties of the Bayesian approach of defining conditional dependencies among parame…