Publications related to Bayesian multivariate linear regression

Random matrix methods for high-dimensional machine learning models

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

EPFL2024

Reliable uncertainties: Error correlation, rotated error bars, and linear regressions in three-isotope plots and beyond

Reto Georg Trappitsch

Correlated errors of experimental data are a common but often neglected problem in physical sciences. Various tools are provided here for thorough propagation of uncertainties in cases of correlated errors. Discussed are techniques especially applicable to ...

ELSEVIER2023

Genomic Diversity of Torque Teno Virus in Blood Samples from Febrile Paediatric Outpatients in Tanzania: A Descriptive Cohort Study

Mary-Anne Hartley

Torque teno virus (TTV) is considered to be an ubiquitous member of the commensal human blood virome commonly reported in mixed genotype co-infections. This study investigates the genomic diversity of TTV in blood samples from 816 febrile Tanzanian childre ...

MDPI2022

Hands-on reservoir computing: a tutorial for practical implementation

Matteo Cucchi, Giuseppe Ciccone

This manuscript serves a specific purpose: to give readers from fields such as material science, chemistry, or electronics an overview of implementing a reservoir computing (RC) experiment with her/his material system. Introductory literature on the topic ...

IOP Publishing Ltd2022

Impact of adherence to a lifestyle-integrated programme on physical function and behavioural complexity in young older adults at risk of functional decline: a multicentre RCT secondary analysis

Kamiar Aminian, Anisoara Ionescu

Context Long-term adherence to physical activity (PA) interventions is challenging. The Lifestyle-integrated Functional Exercise programmes were adapted Lifestyle-integrated Functional Exercise (aLiFE) to include more challenging activities and a behaviour ...

BMJ PUBLISHING GROUP2022

Discrete-Choice Mining of Social Processes

Victor Kristof

Poor decisions and selfish behaviors give rise to seemingly intractable global problems, such as the lack of transparency in democratic processes, the spread of conspiracy theories, and the rise in greenhouse gas emissions. However, people are more predict ...

EPFL2021

Multi-ion-sensing emulator and multivariate calibration optimization by machine learning models

Giovanni De Micheli, Sandro Carrara, Mandresy Ivan Ny Hanitra, Francesca Criscuolo

One paramount challenge in multi-ion-sensing arises from ion interference that degrades the accuracy of sensor calibration. Machine learning models are here proposed to optimize such multivariate calibration. However, the acquisition of big experimental da ...

2021

Numerical investigation on the volute cutwater for pumps running in turbine mode

Elena Vagnoni, Alessandro Morabito

The hydropower sector has recently raised the interest in pump as turbine (PaT) that can be a valid trade-off between capital cost and performance in micro-scale installations. Nevertheless, the modest efficiency of PaTs often restricts their exploitation. ...

Pergamon-Elsevier Science Ltd2021

Radical Intangibles: Materializing the Ephemeral

Sarah Irene Brutton Kenderdine, Lillian Hibberd, Jeffrey Shaw

New materialism considers that the world and its histories are produced by a range of material forces that extend from the physical and the biological to the psychological, social and cultural. In recognizing that heritage is not held in objects alone, new ...

2021

Structure-property maps with Kernel principal covariates regression

Michele Ceriotti, Guillaume André Jean Fraux, Benjamin Aaron Helfrecht

Data analyses based on linear methods constitute the simplest, most robust, and transparent approaches to the automatic processing of large amounts of data for building supervised or unsupervised machine learning models. Principal covariates regression (PC ...

2020

Bayesian regression explains how human participants handle parameter uncertainty

Michael Herzog, Jean-Pascal Théodor Pfister, Mattew Pachai, Maya Anna Jastrzebowska

Author summary How do humans make prediction when the critical factor that influences the quality of the prediction is hidden? Here, we address this question by conducting a simple psychophysical experiment in which participants had to extrapolate a parabo ...

2020

Bayesian modeling of brain function and behavior

Maya Anna Jastrzebowska

The application of Bayesian modeling techniques is increasingly common in neuroscience due to the coherent and principled way in which the paradigm deals with uncertainty. The Bayesian framework is particularly valuable in the context of complex, ill-posed ...

EPFL2020

Modifying Stride Length in Isolation and in Combination With Foot Progression Angle and Step Width Can Improve Knee Kinetics Related to Osteoarthritis; A Preliminary Study in Healthy Subjects

Brigitte Jolles-Haeberli

The purpose of this study was to determine the effects of modifying stride length (SL) on knee adduction and flexion moments, two markers of knee loading associated with medial-compartment knee osteoarthritis (OA) progression. This study also tested if SL ...

2020

An automated calibration framework and open source tools for 3D lake hydrodynamic models

Alfred Johny Wüest, Damien Bouffard, Andrea Cimatoribus, Theo Baracchini

Understanding lake dynamics is crucial to provide scientifically credible information for ecosystem management. In this context, three-dimensional hydrodynamic models are a key information source to assess critical but often subtle changes in lake dynamics ...

2020

Dynamic wetting in pressure-infiltration of ceramic particle preforms by molten metal

Gionata Schneider

This thesis investigates the interaction between mechanically induced flow of a liquid metal into a porous solid, and kinetic effects at the triple line. The approach is experimental and focuses on metal-ceramic systems for which the literature gives value ...

EPFL2019

Methodology And Convergence Rates For Functional Time Series Regression

Victor Panaretos, Tung Huy Pham

The functional linear model extends the notion of linear regression to the case where the response and covariates are iid elements of an infinite-dimensional Hilbert space. The unknown to be estimated is a Hilbert-Schmidt operator, whose inverse is by defi ...

STATISTICA SINICA2018

Mapping and characterization of positive and negative BOLD responses to visual stimulation in multiple brain regions at 7T

Rolf Gruetter, Wietske Van der Zwaag, João Pedro Forjaco Jorge, Patricia Figueiredo

External stimuli and tasks often elicit negative BOLD responses in various brain regions, and growing experimental evidence supports that these phenomena are functionally meaningful. In this work, the high sensitivity available at 7T was explored to map an ...

Wiley2018

The SDSS-IV eBOSS: emission line galaxy catalogues at z approximate to 0.8 and study of systematic errors in the angular clustering

Jean-Paul Richard Kneib, Johan Comparat, Anand Stéphane Raichoor, Xiaoqing Zhou, Timothée Guy Olivier Delubac

We present two wide-field catalogues of photometrically selected emission line galaxies (ELGs) at z approximate to 0.8 covering about 2800 deg(2)over the south galactic cap. The catalogues were obtained using a Fisher discriminant technique described in a ...

Oxford Univ Press2017

Understanding and Decoding Imagined Speech using Electrocorticographic Recordings in Humans

Stéphanie Martin

Certain brain disorders, resulting from brainstem infarcts, traumatic brain injury, stroke and amyotrophic lateral sclerosis, limit verbal communication despite the patient being fully aware. People that cannot communicate due to neurological disorders wou ...

EPFL2017

Updates to the ASCE-41-13 Provisions for the Nonlinear Modeling of Steel Wide-Flange Columns for Performance-based Earthquake Engineering

Dimitrios Lignos, Alexander Riley Hartloper

Performance-based earthquake engineering requires numerical models that are able to reliably predict the expected component behavior of buildings under seismic excitations. The nonlinear modeling guidelines provided in ASCE-41-13 have historically been use ...

2017