Concept

Loi gamma-normale

Publications associées (86)

Integrating the Finite Element Method with a Data-Driven Approach for Dam Displacement Prediction

Both numerical simulations and data-driven methods have been applied in dam's displacement modeling. For monitored displacement data-driven methods, the physical mechanism and structural correlations were rarely discussed. In order to take the spatial and ...

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

Robust Generalization via f−Mutual Information

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

Given two probability measures P and Q and an event E, we provide bounds on P(E) in terms of Q(E) and f-divergences. In particular, the bounds are instantiated when the measures considered are a joint distribution and the corresponding product of marginals ...

IEEE2020

An Inertial Parallel and Asynchronous Forward-Backward Iteration for Distributed Convex Optimization

Colin Neil Jones, Georgios Stathopoulos

Two characteristics that make convex decomposition algorithms attractive are simplicity of operations and generation of parallelizable structures. In principle, these schemes require that all coordinates update at the same time, i.e., they are synchronous ...

SPRINGER/PLENUM PUBLISHERS2019

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

Zhenzhu Meng, Yating Hu, Chenfei Shao

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

Particle number control for direct simulation Monte-Carlo methodology using kernel estimates

The efficiency of stochastic particle schemes for large scale simulations relies on the ability to preserve a uniform distribution of particles in the whole physical domain. While simple particle split and merge algorithms have been considered previously, ...

2019

Sequential Outlier Detection Based on Incremental Decision Trees

Hakan Gökcesu, Kaan Gökcesu

We introduce an online outlier detection algorithm to detect outliers in a sequentially observed data stream. For this purpose, we use a two-stage filtering and hedging approach. In the first stage, we construct a multimodal probability density function to ...

2019

Comparison of data interpretation methodologies for geotechnical back analysis

Ian Smith

Rigorous back analysis plays a major role in providing information to engineers for better decision-making. Most research on this topic has focused on optimization techniques. Comparative studies of data interpretation methodologies have seldom been report ...

2019

Episodic memory decline in Parkinson' s disease: relation with white matter hyperintense lesions and influence of quantification method

Meritxell Bach Cuadra, Tobias Kober, Bénédicte Marie Maréchal, Mario Joao Fartaria de Oliveira

The relation of white matter hyperintense lesions to episodic memory impairment in patients with Parkinson's disease (PD) is still controversial. We aimed at evaluating the relation between white matter hyperintense lesions and episodic memory decline in p ...

SPRINGER2019

Wasserstein Distributionally Robust Kalman Filtering

Daniel Kuhn, Viet Anh Nguyen, Soroosh Shafieezadeh Abadeh, Peyman Mohajerin Esfahani

We study a distributionally robust mean square error estimation problem over a nonconvex Wasserstein ambiguity set containing only normal distributions. We show that the optimal estimator and the least favorable distribution form a Nash equilibrium. Despit ...

2018

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