Publication

Bayesian Monte Carlo assimilation for the PETALE experimental programme using inter-dosimeter correlation

Andreas Pautz, Vincent Pierre Lamirand, Dimitri Rochman, Axel Guy Marie Laureau
2020
Conference Papers

Abstract

This article presents the methodology developed to generate and use dosimeter covariances and to estimate nuisance parameters for the PETALE experimental programme. In anticipation of the final experimental results, this work investigates the consideration of these experimental correlations in the Bayesian assimilation process on nuclear data. Results show that the assimilation of a given set of dosimeters provides a strong constraint on some of the posterior reaction rate predictions of the other dosimeters. It confirms that, regarding the assimilation process, the different sets of dosimeters are correlated.

Official source

https://infoscience.epfl.ch/entities/publication/248aed26-ffce-468b-b7b4-58ecccb41ca5

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Andreas Pautz, Vincent Pierre Lamirand, Dimitri Rochman, Axel Guy Marie Laureau
2020
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/248aed26-ffce-468b-b7b4-58ecccb41ca5

About this result

Related concepts (19)

Data assimilation

Data assimilation is a mathematical discipline that seeks to optimally combine theory (usually in the form of a numerical model) with observations. There may be a number of different goals sought – for example, to determine the optimal state estimate of a system, to determine initial conditions for a numerical forecast model, to interpolate sparse observation data using (e.g. physical) knowledge of the system being observed, to set numerical parameters based on training a model from observed data.

Cultural assimilation

Cultural assimilation is the process in which a minority group or culture comes to resemble a society's majority group or assimilate the values, behaviors, and beliefs of another group whether fully or partially. The different types of cultural assimilation include full assimilation and forced assimilation. Full assimilation is the more prevalent of the two, as it occurs spontaneously. When used as a political ideology, assimilationism refers to governmental policies of deliberately assimilating ethnic groups into the national culture.

Forced assimilation

Forced assimilation is the involuntary cultural assimilation of religious or ethnic minority groups, during which they are forced to adopt the language, national identity, norms, mores, customs, traditions, values, mentality, perceptions, way of life, and often the religion and ideology of an established and generally larger community belonging to dominant culture by a government. The enforced use of a dominant language in legislation, education, literature, and worship also counts as forced assimilation.

Related publications (6)

A Bayesian data assimilation framework for lake 3D hydrodynamic models with a physics-preserving particle filtering method using SPUX-MITgcm v1

Damien Bouffard, Firat Ozdemir, Cintia Luz Ramon Casanas, Camille Roland Marie Minaudo

We present a Bayesian inference for a three-dimensional hydrodynamic model of Lake Geneva with stochastic weather forcing and high-frequency observational datasets. This is achieved by coupling a Bayesian inference package, SPUX, with a hydrodynamics packa ...

COPERNICUS GESELLSCHAFT MBH2022

Radar-rain gauge merging and discharge data assimilation for flood forecasting in Alpine catchments

Alain Tommy Foehn

Floods are responsible for one third of the economic losses induced by natural hazards throughout the world. To better protect the population and infrastructures, flood forecasting systems make us of weather forecasts to foresee floods several days in adva ...

EPFL2019

Robust Biophysical Parameter Estimation with a Neural Network Enhanced Hamiltonian Markov Chain Monte Carlo Sampler

Jean-Philippe Thiran, Gabriel Girard, Jonathan Rafael Patino Lopez, Erick Jorge Canales Rodriguez, Marco Pizzolato, Thomas Yu

Probabilistic parameter estimation in model fitting runs the gamut from maximum likelihood or maximum a posteriori point estimates from optimization to Markov Chain Monte Carlo (MCMC) sampling. The latter, while more computationally intensive, generally pr ...

SPRINGER INTERNATIONAL PUBLISHING AG2019