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Bayesian uncertainty management in temporal dependence of extremes

Related publications (31)

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Auto-tuning deep forest for shear stiffness prediction of headed stud connectors

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The shear stiffness of headed stud connector is a critical parameter for the calculation of deflection and inter-facial shear force for steel-concrete composite structure. Thus, this study presented a promising data-driven model auto-tuning Deep Forest (AT ...

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Cemented granular materials are abundant in nature and are often artificially produced. Their macroscopic behaviour is driven by small-scale material processes, which are generally classified as: grain breakage, cement damage and fragment rearrangement. Th ...

SPRINGER HEIDELBERG2022

Functional estimation of anisotropic covariance and autocovariance operators on the sphere

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2022

SPHARMA approximations for stationary functional time series on the sphere

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In this paper, we focus on isotropic and stationary sphere-cross-time random fields. We first introduce the class of spherical functional autoregressive-moving average processes (SPHARMA), which extend in a natural way the spherical functional autoregressi ...

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Sparsely Observed Functional Time Series: Theory and Applications

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Functional time series is a temporally ordered sequence of not necessarily independent random curves. While the statistical analysis of such data has been traditionally carried out under the assumption of completely observed functional data, it may well ha ...

EPFL2021

Drift Estimation of Multiscale Diffusions Based on Filtered Data

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Learning Self-Exciting Temporal Point Processes Under Noisy Observations

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Understanding the diffusion patterns of sequences of interdependent events is a central question for a variety of disciplines. Temporal point processes are a class of elegant and powerful models of such sequences; these processes have become popular across ...

EPFL2021

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Probabilistic and Bayesian methods for uncertainty quantification of deterministic and stochastic differential equations

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In this thesis we explore uncertainty quantification of forward and inverse problems involving differential equations. Differential equations are widely employed for modeling natural and social phenomena, with applications in engineering, chemistry, meteor ...

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Aerosol formation and growth rates from chamber experiments using Kalman smoothing

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Copernicus Publications2021