Publications related to Conditionally least-informative distributions: The case of the location parameter

Filtered data and eigenfunction estimators for statistical inference of multiscale and interacting diffusion processes

We study the problem of learning unknown parameters of stochastic dynamical models from data. Often, these models are high dimensional and contain several scales and complex structures. One is then interested in obtaining a reduced, coarse-grained descript ...

EPFL2022

Information content and aerosol property retrieval potential for different types of in situ polar nephelometer data

Alireza Moallemi, Robin Lewis Modini, Philippe Giaccari

Polar nephelometers are in situ instruments used to measure the angular distribution of light scattered by aerosol particles. These types of measurements contain substantial information about the properties of the aerosol being probed (e.g. concentrations, ...

2022

Practical issues with modeling extreme Brazilian rainfall

Anthony Christopher Davison, Isolde Santos Previdelli, Paulo Vitor Da Costa Pereira

Accurately quantifying extreme rainfall is important for the design of hydraulic structures, for flood mapping and zoning and for disaster management. In order to produce maps of estimates of 25-year rainfall return levels in Brazil, we selected 893 shorte ...

BRAZILIAN STATISTICAL ASSOCIATION2021

Revisiting the Calculation of the Early Time HEMP Conducted Environment

Carlos Alberto Romero Romero, Nicolas Mora Parra

The early time high altitude electromagnetic pulse conducted environment calculation is revisited by using the transmission line (TL) theory and including high-frequency corrections that were not present in the earlier studies. Waveform salient parameters, ...

2021

A non-intrusive multifidelity method for the reduced order modeling of nonlinear problems

Jan Sickmann Hesthaven, Mariella Kast, Mengwu Guo

We propose a non-intrusive reduced basis (RB) method for parametrized nonlinear partial differential equations (PDEs) that leverages models of different accuracy. From a collection of low-fidelity (LF) snapshots, parameter locations are extracted for the e ...

ELSEVIER SCIENCE SA2020

A sequence-dependent coarse-grain model of B-DNA with explicit description of bases and phosphate groups parametrised from large scale Molecular Dynamics simulations

Alessandro Samuele Patelli

We introduce a sequence-dependent coarse-grain model of double-stranded DNA with an explicit description of both the bases and the phosphate groups as interacting rigid-bodies. The model parameters are trained on extensive, state-of-the-art large scale mol ...

EPFL2019

Multi-Modal Mean-Fields via Cardinality-Based Clamping

Pascal Fua, François Fleuret

Mean Field inference is central to statistical physics. It has attracted much interest in the Computer Vision community to efficiently solve problems expressible in terms of large Conditional Random Fields. However, since it models the posterior probabilit ...

Ieee2017

Multi-Modal Mean-Fields via Cardinality-Based Clamping

Pascal Fua, François Fleuret

Mean Field inference is central to statistical physics. It has attracted much interest in the Computer Vision community to efficiently solve problems expressible in terms of large Conditional Random Fields. However, since it models the posterior probabilit ...

Ieee2017

Renormalization and blow up for wave maps from $S^2 \times \mathbb{R}$ to $S^2$

Sohrab Mirshams Shahshahani

We construct a one parameter family of nite time blow ups to the co-rotational wave maps problem from

S^2\times R

to

S^2

parameterized by

\nu \epsilon (\frac{1}{2},1]

. The longitudinal function

u(t,\alpha)

which is the main object of study will be ...

2016

RENORMALIZATION AND BLOW-UP FOR WAVE MAPS FROM S-2 x R TO S-2

Sohrab Mirshams Shahshahani

We construct a one parameter family of finite time blow-ups to the co-rotational wave maps problem from S-2 x R to S-2, parameterized by nu is an element of (1/2, 1]. The longitudinal function u(t, alpha) which is the main object of study will be obtained ...

American Mathematical Society2016

Conditionally least-informative distributions: The case of the location parameter

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Filtered data and eigenfunction estimators for statistical inference of multiscale and interacting diffusion processes

Information content and aerosol property retrieval potential for different types of in situ polar nephelometer data

Practical issues with modeling extreme Brazilian rainfall

Revisiting the Calculation of the Early Time HEMP Conducted Environment

A non-intrusive multifidelity method for the reduced order modeling of nonlinear problems

A sequence-dependent coarse-grain model of B-DNA with explicit description of bases and phosphate groups parametrised from large scale Molecular Dynamics simulations

Multi-Modal Mean-Fields via Cardinality-Based Clamping

Multi-Modal Mean-Fields via Cardinality-Based Clamping

Renormalization and blow up for wave maps from $S^2 \times \mathbb{R}$ to $S^2$

RENORMALIZATION AND BLOW-UP FOR WAVE MAPS FROM S-2 x R TO S-2

Multi-Modal Mean-Fields via Cardinality-Based Clamping

Multi-Modal Mean-Fields via Cardinality-Based Clamping

Information content and aerosol property retrieval potential for different types of in situ polar nephelometer data

A sequence-dependent coarse-grain model of B-DNA with explicit description of bases and phosphate groups parametrised from large scale Molecular Dynamics simulations

A non-intrusive multifidelity method for the reduced order modeling of nonlinear problems

Practical issues with modeling extreme Brazilian rainfall

Revisiting the Calculation of the Early Time HEMP Conducted Environment

Filtered data and eigenfunction estimators for statistical inference of multiscale and interacting diffusion processes

Renormalization and blow up for wave maps from $S^2 \times \mathbb{R}$ to $S^2$

RENORMALIZATION AND BLOW-UP FOR WAVE MAPS FROM S-2 x R TO S-2