Search results for "Import"

A dempster-shafer theory-based approach to compute the birnbaum importance measure under epistemic uncertainty

2016

Importance Measures (IMs) aim at quantifying the contribution of components to the system performance. In Process Risk Assessment (PRA), they are commonly used by risk managers to derive information about the risk/safety significance of events. However, IMs are typically calculated without taking into account the uncertainty that inevitably occurs whenever the input reliability data are poor. In literature, uncertainty arising from the lack of knowledge on the system/process parameters is defined as epistemic or subjective uncertainty. The present work aims at investigating on its influence on the Birnbaum IM and on how such an uncertainty could be accounted for in the components ranking. I…

Ethics of Artificial Intelligence : Research Challenges and Potential Solutions

2020

Artificial Intelligence (AI) is a rapidly emerging paradigm with many applications in healthcare, industries, and smart cities. However, this rise of global interest in AI has fueled a renewed interest from the public sector and global policymakers. As AI networks (e.g., chatbots, automation systems, and helping agents) are paving their way as interactive household items, a critically important research issue is understanding the ethical impact of these autonomous agents. What is the explanation of the AI decision-making process? What are the legal, societal, and moral consequences of these decisions and actions? Should these AI systems be allowed to make decisions for human beings and to w…

Net satisfaction: a different point of view on the measurement of subjective well-being

2013

Starting from the baffling relationship between satisfaction and importance in the evaluation of subjective well-being, this paper presents a new definition of satisfaction useful to build a composite index of subjective well-being. The index is proven on data from European Quality of Life Survey 2007 and compared with two of the standard literature approaches to measure satisfaction taking into account importance weighting.

The development of national and European identity among children living in Italy: A cross-cultural comparison

2008

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…

Group Importance Sampling for particle filtering and MCMC

2018

Bayesian methods and their implementations by means of sophisticated Monte Carlo techniques have become very popular in signal processing over the last years. Importance Sampling (IS) is a well-known Monte Carlo technique that approximates integrals involving a posterior distribution by means of weighted samples. In this work, we study the assignation of a single weighted sample which compresses the information contained in a population of weighted samples. Part of the theory that we present as Group Importance Sampling (GIS) has been employed implicitly in different works in the literature. The provided analysis yields several theoretical and practical consequences. For instance, we discus…

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…

Heretical Mutiple Importance Sampling

2016

Multiple Importance Sampling (MIS) methods approximate moments of complicated distributions by drawing samples from a set of proposal distributions. Several ways to compute the importance weights assigned to each sample have been recently proposed, with the so-called deterministic mixture (DM) weights providing the best performance in terms of variance, at the expense of an increase in the computational cost. A recent work has shown that it is possible to achieve a trade-off between variance reduction and computational effort by performing an a priori random clustering of the proposals (partial DM algorithm). In this paper, we propose a novel "heretical" MIS framework, where the clustering …

Unbiased Inference for Discretely Observed Hidden Markov Model Diffusions

2021

We develop a Bayesian inference method for diffusions observed discretely and with noise, which is free of discretisation bias. Unlike existing unbiased inference methods, our method does not rely on exact simulation techniques. Instead, our method uses standard time-discretised approximations of diffusions, such as the Euler--Maruyama scheme. Our approach is based on particle marginal Metropolis--Hastings, a particle filter, randomised multilevel Monte Carlo, and importance sampling type correction of approximate Markov chain Monte Carlo. The resulting estimator leads to inference without a bias from the time-discretisation as the number of Markov chain iterations increases. We give conver…