Concept
Empirical probability
Related publications (128)
MATHICSE Technical Report : Analysis-aware defeaturing: problem setting and a posteriori estimation
Annalisa Buffa, Rafael Vazquez Hernandez, Ondine Gabrielle Chanon
Defeaturing consists in simplifying geometrical models by removing the geometrical features that are considered not relevant for a given simulation. Feature removal and simplification of computer-aided design models enables faster simulations for engineeri ...
EPFL2021Learning in Volatile Environments With the Bayes Factor Surprise
Wulfram Gerstner, Johanni Michael Brea, Alireza Modirshanechi, Vasiliki Liakoni
Surprise-based learning allows agents to rapidly adapt to nonstationary stochastic environments characterized by sudden changes. We show that exact Bayesian inference in a hierarchical model gives rise to a surprise-modulated trade-off between forgetting o ...
MIT PRESS2021Bayesian Methods for the Identification of Distribution Networks
The increasing integration of intermittent renewable generation, especially at the distribution level, necessitates advanced planning and optimisation methodologies contingent on the knowledge of the admittance matrix, capturing the topology and line param ...
IEEE2021From 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 ...
2020Wasserstein Distributionally Robust Learning
Many decision problems in science, engineering, and economics are affected by uncertainty, which is typically modeled by a random variable governed by an unknown probability distribution. For many practical applications, the probability distribution is onl ...
EPFL2020A t-distribution based operator for enhancing out of distribution robustness of neural network classifiers
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 ...
IEEE2020Staggered flux state for rectangular-lattice spin-1/2 Heisenberg antiferromagnets
Henrik Moodysson Rønnow, Dmitri Ivanov, Bastien Dalla Piazza, Noore Elahi Shaik
We investigate the spin-1/2 Heisenberg model on a rectangular lattice, using the Gutzwiller projected variational wave function known as the staggered flux state. Using Monte Carlo techniques, the variational parameters and instantaneous spin-spin correlat ...
AMER PHYSICAL SOC2020Context effects on probability estimation
Many decisions rely on how we evaluate potential outcomes and estimate their corresponding probabilities of occurrence. Outcome evaluation is subjective because it requires consulting internal preferences and is sensitive to context. In contrast, probabili ...
PUBLIC LIBRARY SCIENCE2020Regularization via Mass Transportation
Daniel Kuhn, Soroosh Shafieezadeh Abadeh, Peyman Mohajerin Esfahani
The goal of regression and classification methods in supervised learning is to minimize the empirical risk, that is, the expectation of some loss function quantifying the prediction error under the empirical distribution. When facing scarce training data, ...
2019A Bayesian Approach To Inter-Task Fusion For Speaker Recognition
In i-vector based speaker recognition systems, back-end classifiers are trained to factor out nuisance information and retain only the speaker identity. As a result, variabilities arising due to gender, language and accent ( among many others) are suppress ...
IEEE2019