Search results for "Importance"

Group Metropolis Sampling

2017

Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. Two well-known class of MC methods are the Importance Sampling (IS) techniques and the Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce the Group Importance Sampling (GIS) framework where different sets of weighted samples are properly summarized with one summary particle and one summary weight. GIS facilitates the design of novel efficient MC techniques. For instance, we present the Group Metropolis Sampling (GMS) algorithm which produces a Markov chain of sets of weighted samples. GMS in general outperforms other multiple try schemes…

Theoretical Foundations of the Monte Carlo Method and Its Applications in Statistical Physics

2002

In this chapter we first introduce the basic concepts of Monte Carlo sampling, give some details on how Monte Carlo programs need to be organized, and then proceed to the interpretation and analysis of Monte Carlo results.

Sul rilievo costituzionale della responsabilità dirigenziale nell’attuale sistema dei rapporti tra politica e amministrazione

2019

L'articolo intende mettere in luce gli elementi caratterizzanti ed innovativi della responsabilità per risultati dei dirigenti pubblici italiani ed il ruolo di garanzia costituzionale che questa nuova forma di responsabilità può assolvere nell'attuale assetto dei rapporti tra politica e amministrazione. This paper argues for the Constitutional Importance of Accountability for Performance of Public Management in the Italian Legal System.

Tratamiento jurídico de una enfermedad social. Los trastornos de la conducta alimentaria (TCA)

2018

The boundless increase of people suffering from an Eating Disorder (ED), makes necessary to develop measures related to other factors, in order to overcome the ones characterized of being strictly sanitary. The disease has become a social and cultural phenomenon. The exaltation of the cult to the body, with the stereotype of the extreme thinness linked with beauty and success is causing that, particularly young people, try to achieve an image which, in many cases, can jeopardize their health, that is the reason why the rearrangement of values has become an objective of paramount importance. Therefore, the fight against this social illness requires legal actions in order to prevent it from g…

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…

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…