Publications associées à Overcoming the limitations of traditional model-updating approaches

An improved statistical wake meandering model

Fernando Porté Agel, Corey Dean Markfort, Peter Andreas Brugger

A new statistical wake meandering (SWM) model is proposed that improves on existing models in the literature. Compared to the existing SWM models, the proposed model has a closed description that does not require simulations to create look-up tables while ...

IOP Publishing2024

Robust discrete choice models with t-distributed kernel errors

Michel Bierlaire, Prateek Bansal, Thomas Gasos

Outliers in discrete choice response data may result from misclassification and misreporting of the response variable and from choice behaviour that is inconsistent with modelling assumptions (e.g. random utility maximisation). In the presence of outliers, ...

2023

Online interoperable resources for building hippocampal neuron models via the Hippocampus Hub

Felix Schürmann, Armando Romani, Michele Migliore, Luca Leonardo Bologna

To build biophysically detailed models of brain cells, circuits, and regions, a data-driven approach is increasingly being adopted. This helps to obtain a simulated activity that reproduces the experimentally recorded neural dynamics as faithfully as possi ...

2023

Statistical Emulation of Neural Simulators: Application to Neocortical L2/3 Large Basket Cells

Idan Segev, Werner Alfons Hilda Van Geit, Oren Amsalem

Many scientific systems are studied using computer codes that simulate the phenomena of interest. Computer simulation enables scientists to study a broad range of possible conditions, generating large quantities of data at a faster rate than the laboratory ...

FRONTIERS MEDIA SA2022

Sub‐asymptotic motivation for new conditional multivariate extreme models

Anthony Christopher Davison, Thomas Lugrin, Jonathan A. Tawn

Statistical models for extreme values are generally derived from non-degenerate probabilistic limits that can be used to approximate the distribution of events that exceed a selected high threshold. If convergence to the limit distribution is slow, then th ...

2021

A Modular Workflow for Model Building, Analysis, and Parameter Estimation in Systems Biology and Neuroscience

Daniel Keller, Andrii Stepaniuk

Neuroscience incorporates knowledge from a range of scales, from single molecules to brain wide neural networks. Modeling is a valuable tool in understanding processes at a single scale or the interactions between two adjacent scales and researchers use a ...

HUMANA PRESS INC2022

Considerations of the Impacts of Cell-Specific Growth and Production Rate on Clone Selection-A Simulation Study

Florian Maria Wurm

For the manufacturing of complex biopharmaceuticals using bioreactors with cultivated mammalian cells, high product concentration is an important objective. The phenotype of the cells in a reactor plays an important role. Are clonal cell populations showin ...

2021

A Variational Inference Approach to Learning Multivariate Wold Processes

Patrick Thiran, Matthias Grossglauser, Seyed Jalal Etesami, William Trouleau, Negar Kiyavash

Temporal point-processes are often used for mathematical modeling of sequences of discrete events with asynchronous timestamps. We focus on a class of temporal point-process models called multivariate Wold processes (MWP). These processes are well suited t ...

MICROTOME PUBLISHING2021

Inference on the Angular Distribution of Extremes

Claudio Andri Semadeni

The spectral distribution plays a key role in the statistical modelling of multivariate extremes, as it defines the dependence structure of multivariate extreme-value distributions and characterizes the limiting distribution of the relative sizes of the co ...

EPFL2020

A new spatial count data model with Bayesian additive regression trees for accident hot spot identification

Prateek Bansal, Rico Krüger

The identification of accident hot spots is a central task of road safety management. Bayesian count data models have emerged as the workhorse method for producing probabilistic rankings of hazardous sites in road networks. Typically, these methods assume ...

2020

Motivating Innovation: The Effect of Loss Aversion on the Willingness to Persist

Kenneth Younge

We investigate the willingness of individuals to persist at exploration in the face of failure. Prior research suggests that the organization's "tolerance for failure" may motivate greater exploration by the individual. Little is known, however, about how ...

MIT Press2020

Contributions to Likelihood-Based Modelling of Extreme Values

Léo Raymond-Belzile

xtreme value analysis is concerned with the modelling of extreme events such as floods and heatwaves, which can have large impacts. Statistical modelling can be useful to better assess risks even if, due to scarcity of measurements, there is inherently ver ...

EPFL2019

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

Data-Interpretation Methodologies for Practical Asset-Management

Ian Smith, Yves Sylvain Gilles Reuland, Sai Ganesh Sarvotham Pai

Monitoring and interpreting structural response using structural-identification methodologies improves understanding of civil-infrastructure behavior. New sensing devices and inexpensive computation has made model-based data interpretation feasible in engi ...

2019

The needle model: A new model for the main hydration peak of alite

Karen Scrivener, Alexandre Reda Constantin William Caleb Ouzia

This article presents a new model for the main hydration peaks of tricalcium silicate and alite on the assumption that C-S-H nucleates and grows as needles. The model relies only on directly observable quantities and reproduces the transition from the acce ...

Pergamon-Elsevier Science Ltd2019

Concrete Dam Displacement Prediction Based on an ISODATA-GMM Clustering and Random Coefficient Model

Zhenzhu Meng, Chenfei Shao, Yating Hu

Displacement data modelling is of great importance for the safety control of concrete dams. The commonly used artificial intelligence method modelled the displacement data at each monitoring point individually, i.e., the data correlations between the monit ...

2019

Efficient Learning from Comparisons

Lucas Maystre

Humans are comparison machines: comparing and choosing an item among a set of alternatives (such as objects or concepts) is arguably one of the most natural ways for us to express our preferences and opinions. In many applications, the analysis of data con ...

EPFL2018

Functional Peaks-Over-Threshold Analysis for Complex Extreme Events

Most current risk assessment for complex extreme events relies on catalogues of similar events, either historical or generated artificially. In the latter, no existing methods produce completely new events with mathematically justified extrapolation above ...

EPFL2018

Semiparametric Bayesian Risk Estimation for Complex Extremes

Thomas Lugrin

Extreme events are responsible for huge material damage and are costly in terms of their human and economic impacts. They strike all facets of modern society, such as physical infrastructure and insurance companies through environmental hazards, banking an ...

EPFL2018

Comparing methodologies for structural identification and fatigue life prediction of a highway bridge

Ian Smith, Alain Nussbaumer, Sai Ganesh Sarvotham Pai

Accurate measurement-data interpretation leads to increased understanding of structural behavior and enhanced asset-management decision making. In this paper, four data-interpretation methodologies, residual minimization, traditional Bayesian model updatin ...

2018