Concept

Parameter space

Publications associées (25)

Fitting summary statistics of neural data with a differentiable spiking network simulator

Wulfram Gerstner, Johanni Michael Brea, Alireza Modirshanechi, Shuqi Wang

Fitting network models to neural activity is an important tool in neuroscience. A popular approach is to model a brain area with a probabilistic recurrent spiking network whose parameters maximize the likelihood of the recorded activity. Although this is w ...

2021

Amplitude analysis for the measurement of the photon polarisation in B rightarrow K pi pi gamma decays

Violaine Bellée

This work proposes a method to measure the photon polarisation in

B^+ \rightarrow K^+ \pi^- \pi^+ \gamma

decays and prepares the necessary elements for this measurement using the data sets collected by the LHCb experiment at CERN in

2011

2012

, $2016 ...

EPFL2020

An Easily Computable Error Estimator In Space And Time For The Wave Equation

Marco Picasso, Olga Gorynina

We propose a cheaper version of a posteriori error estimator from Gorynina et al. (Namer. Anal. (2017)) for the linear second-order wave equation discretized by the Newmark scheme in time and by the finite element method in space. The new estimator preserv ...

EDP Sciences2019

Accurate and efficient interpretation of load-test data for asset-management

Sai Ganesh Sarvotham Pai

Increasing demand for new infrastructure and ageing of existing infrastructure has made management of infrastructure a key challenge of this century. Replacement of all ageing civil infrastructure is economically and environmentally unsustainable. Civil in ...

EPFL2019

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

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

A taxonomy for Whole Building Life Cycle Assessment (WBLCA)

Catherine Elvire L. De Wolf

Purpose The purpose of this paper is to present an analysis of common parameters in existing tools that provide guidance to carry out Whole Building Life Cycle Assessment (WBLCA) and proposes a new taxonomy, a catalogue of parameters, for the definition of ...

EMERALD GROUP PUBLISHING LTD2019

Estimation of groundwater storage from seismic data using deep learning

Jan Sickmann Hesthaven

We investigate the feasibility of the use of convolutional neural networks to estimate the amount of groundwater stored in the aquifer and delineate water-table level from active-source seismic data. The seismic data to train and test the neural networks a ...

Wiley2019

Deep convolutional neural networks for estimating porous material parameters with ultrasound tomography

Jan Sickmann Hesthaven

We study the feasibility of data based machine learning applied to ultrasound tomography to estimate water-saturated porous material parameters. In this work, the data to train the neural networks is simulated by solving wave propagation in coupled porovis ...

2018

Cosmological constraints from the convergence 1-point probability distribution

Jonathan Andrew Blazek

We examine the cosmological information available from the 1-point probability density function (PDF) of the weak-lensing convergence field, utilizing fast L-PICOLA simulations and a Fisher analysis. We find competitive constraints in the Omega(m)-sigma(8) ...

Oxford Univ Press2017

Approximate maximum likelihood estimation for population genetic inference

Gregory Bruce Ewing

In many population genetic problems, parameter estimation is obstructed by an intractable likelihood function. Therefore, approximate estimation methods have been developed, and with growing computational power, sampling-based methods became popular. Howev ...

De Gruyter2017

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