Publications related to Bayesian Calibration of Thermal-Hydraulics Model with Time-Dependent Output

Efficient ensemble summaries are inversely related to visual crowding

Michael Herzog, David Pascucci, Yury Markov, Natalia Tiurina

Visual crowding is the inability to perceive properly peripheral stimuli within clutter. Previous work has shown that crowding is affected by perceptual grouping: when the flankers do not group with the target, crowding decreases, leading to uncrowding. Ty ...

2021

COVID-19: data-driven dynamic asset allocation in times of pandemic

Anna Timonina-Farkas

The COVID-19 pandemic has demonstrated the importance and value of multi-period asset allocation strategies responding to rapid changes in market behavior. In this article, we formulate and solve a multi-stage stochastic optimization problem, choosing the ...

AMER INST MATHEMATICAL SCIENCES-AIMS2021

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

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

A t-distribution based operator for enhancing out of distribution robustness of neural network classifiers

Philip Neil Garner

Neural Network (NN) classifiers can assign extreme probabilities to samples that have not appeared during training (out-of-distribution samples) resulting in erroneous and unreliable predictions. One of the causes for this unwanted behaviour lies in the us ...

IEEE2020

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Serge Vaudenay

The maximal achievable advantage of a (computationally unbounded) distinguisher to determine whether a source

Z

is distributed according to distribution

P_0

or

P_1

, when given access to one sample of

Z

, is characterized by the statistical distance ...

Springer, Cham2020

From Data to Decisions: Distributionally Robust Optimization is Optimal

Daniel Kuhn, Peyman Mohajerin Esfahani

We study stochastic programs where the decision-maker cannot observe the distribution of the exogenous uncertainties but has access to a finite set of independent samples from this distribution. In this setting, the goal is to find a procedure that transfo ...

2020

Bayesian regression explains how human participants handle parameter uncertainty

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

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

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

Scalable Stochastic Optimization: Scenario Reduction with Guarantees

Kilian Schindler

Stochastic optimization is a popular modeling paradigm for decision-making under uncertainty and has a wide spectrum of applications in management science, economics and engineering. However, the stochastic optimization models one faces in practice are int ...

EPFL2020

Bayesian Calibration of Thermal-Hydraulics Model with Time-Dependent Output

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Efficient ensemble summaries are inversely related to visual crowding

COVID-19: data-driven dynamic asset allocation in times of pandemic

Revisiting the Calculation of the Early Time HEMP Conducted Environment

Practical issues with modeling extreme Brazilian rainfall

A t-distribution based operator for enhancing out of distribution robustness of neural network classifiers

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

From Data to Decisions: Distributionally Robust Optimization is Optimal

Bayesian regression explains how human participants handle parameter uncertainty

Inference on the Angular Distribution of Extremes

Scalable Stochastic Optimization: Scenario Reduction with Guarantees

Efficient ensemble summaries are inversely related to visual crowding

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Scalable Stochastic Optimization: Scenario Reduction with Guarantees

Revisiting the Calculation of the Early Time HEMP Conducted Environment

A t-distribution based operator for enhancing out of distribution robustness of neural network classifiers

From Data to Decisions: Distributionally Robust Optimization is Optimal

Inference on the Angular Distribution of Extremes

Bayesian regression explains how human participants handle parameter uncertainty

Practical issues with modeling extreme Brazilian rainfall

COVID-19: data-driven dynamic asset allocation in times of pandemic