Search results for " Monte Carlo"

EXPOSURE OF Gd2O3-ALANINE AND Gd2O3-AMMONIUM TARTRATE ESR DOSIMETERS TO THERMAL NEUTRONS: EXPERIMENTS AND MONTE CARLO SIMULATIONS

2008

Nonlinear impact estimation in spatial autoregressive models

2018

International audience; This paper extends the literature on the calculation and interpretation of impacts for spatial autoregressive models. Using a Bayesian framework, we show how the individual direct and indirect impacts associated with an exogenous variable introduced in a nonlinear way in such models can be computed, theoretically and empirically. Rather than averaging the individual impacts, we suggest to graphically analyze them along with their confidence intervals calculated from Markov chain Monte Carlo (MCMC). We also explicitly derive the form of the gap between individual impacts in the spatial autoregressive model and the corresponding model without a spatial lag and show, in…

Semi-strong inefficiency in the fixed odds betting market: Underestimating the positive impact of head coach replacement in the main European soccer …

2019

Abstract In this paper we analyse the efficiency of the sports betting market, seeking to ascertain whether the market is efficient in the case of fixed odds provided by bookmakers in the four major European soccer leagues under the semi-strong efficiency hypothesis. By examining the trends of odds in the event of a major change in expectations about team results, i.e. when the head coach of a team is replaced, we attempt to verify the argument that a profitable strategy for the bettor is likely to be possible. In this case, the market under consideration would be inefficient. Analysing the average effect of head coach replacement, we find a positive impact on team performance. Based on thi…

Studio di modelli equivalenti per la simulazione con il codice PENELOPE della risposta in efficienza di un rivelatore HPGe

2017

La simulazione della risposta di un rivelatore HPGe con l’impiego di codici Monte Carlo è una tecnica ormai diffusamente impiegata e particolarmente utile per la valutazione di efficienze quando non sono disponibili standards di calibrazione con stessa forma e composizione del campione in esame. Il risultato della simulazione dipende dalla conoscenza più o meno dettagliata delle caratteristiche del rivelatore, atte a definire un “modello” dello stesso. Per evidenziare anche quelle parti non definite nella certificazione del costruttore, solitamente si effettua una radiografia del rivelatore fatta eccezione per i casi in cui non è realizzabile pena lo smontaggio della struttura di schermatur…

Monte Carlo simulation of DNA electrophoresis

1989

This paper describes an attempt to study the electrophoresis mobility of a DNA molecule in a gel by means of a Monte Carlo simulation. We find that the electrophoresis mobility mu can be well described by the empirical equation mu v kappa 1/N + kappa 2E2 with N being the number of monomers of the model chain and E being the applied field. For small E the data can merge into the linear response result mu = kappa 1/N. The paper also discusses necessary extensions of the present approach.

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…

Measurement of the cosmic ray energy spectrum using hybrid events of the Pierre Auger Observatory

2012

The energy spectrum of ultra-high energy cosmic rays above 10$^{18}$ eV is measured using the hybrid events collected by the Pierre Auger Observatory between November 2005 and September 2010. The large exposure of the Observatory allows the measurement of the main features of the energy spectrum with high statistics. Full Monte Carlo simulations of the extensive air showers (based on the CORSIKA code) and of the hybrid detector response are adopted here as an independent cross check of the standard analysis (Phys. Lett. B 685, 239 (2010)). The dependence on mass composition and other systematic uncertainties are discussed in detail and, in the full Monte Carlo approach, a region of confiden…

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…

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…