Zobrazeno 1 - 10
of 449
pro vyhledávání: '"Duncan, Andrew P."'
Energy-based models (EBMs) offer a flexible framework for probabilistic modelling across various data domains. However, training EBMs on data in discrete or mixed state spaces poses significant challenges due to the lack of robust and fast sampling m
Externí odkaz:
http://arxiv.org/abs/2412.01019
Selecting cost-effective optimal sensor configurations for subsequent inference of parameters in black-box stochastic systems faces significant computational barriers. We propose a novel and robust approach, modelling the joint distribution over inpu
Externí odkaz:
http://arxiv.org/abs/2410.12036
Global trade is shaped by a complex mix of factors beyond supply and demand, including tangible variables like transport costs and tariffs, as well as less quantifiable influences such as political and economic relations. Traditionally, economists mo
Externí odkaz:
http://arxiv.org/abs/2409.06554
Autor:
Bull, Lawrence A., Jeon, Chiho, Girolami, Mark, Duncan, Andrew, Schooling, Jennifer, Haro, Miguel Bravo
We suggest a multilevel model, to represent aggregate train-passing events from the Staffordshire bridge monitoring system. We formulate a combined model from simple units, representing strain envelopes (of each train passing) for two types of commut
Externí odkaz:
http://arxiv.org/abs/2403.17820
Structural Health Monitoring (SHM) technologies offer much promise to the risk management of the built environment, and they are therefore an active area of research. However, information regarding material properties, such as toughness and strength
Externí odkaz:
http://arxiv.org/abs/2309.07695
Training energy-based models (EBMs) on discrete spaces is challenging because sampling over such spaces can be difficult. We propose to train discrete EBMs with energy discrepancy (ED), a novel type of contrastive loss functional which only requires
Externí odkaz:
http://arxiv.org/abs/2307.07595
Autor:
Schröder, Tobias, Ou, Zijing, Lim, Jen Ning, Li, Yingzhen, Vollmer, Sebastian J., Duncan, Andrew B.
Energy-based models are a simple yet powerful class of probabilistic models, but their widespread adoption has been limited by the computational burden of training them. We propose a novel loss function called Energy Discrepancy (ED) which does not r
Externí odkaz:
http://arxiv.org/abs/2307.06431
In practice, non-destructive testing (NDT) procedures tend to consider experiments (and their respective models) as distinct, conducted in isolation and associated with independent data. In contrast, this work looks to capture the interdependencies b
Externí odkaz:
http://arxiv.org/abs/2305.08657
Kernelized Stein discrepancy (KSD) is a score-based discrepancy widely used in goodness-of-fit tests. It can be applied even when the target distribution has an unknown normalising factor, such as in Bayesian analysis. We show theoretically and empir
Externí odkaz:
http://arxiv.org/abs/2304.14762
We prove a convergence theorem for U-statistics of degree two, where the data dimension $d$ is allowed to scale with sample size $n$. We find that the limiting distribution of a U-statistic undergoes a phase transition from the non-degenerate Gaussia
Externí odkaz:
http://arxiv.org/abs/2302.05686