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 ...
2021Amplitude analysis for the measurement of the photon polarisation in B rightarrow K pi pi gamma decays
This work proposes a method to measure the photon polarisation in decays and prepares the necessary elements for this measurement using the data sets collected by the LHCb experiment at CERN in , , $2016 ...
EPFL2020An Easily Computable Error Estimator In Space And Time For The Wave Equation
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 Sciences2019Accurate and efficient interpretation of load-test data for asset-management
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 ...
EPFL2019Robust 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 AG2019A taxonomy for Whole Building Life Cycle Assessment (WBLCA)
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 LTD2019Estimation of groundwater storage from seismic data using deep learning
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 ...
Wiley2019Deep convolutional neural networks for estimating porous material parameters with ultrasound tomography
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 ...
2018Cosmological constraints from the convergence 1-point probability distribution
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 Press2017Approximate maximum likelihood estimation for population genetic inference
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