Zobrazeno 1 - 10
of 12 916
pro vyhledávání: '"A, Knoblauch"'
Deterministic mathematical models, such as those specified via differential equations, are a powerful tool to communicate scientific insight. However, such models are necessarily simplified descriptions of the real world. Generalised Bayesian methodo
Externí odkaz:
http://arxiv.org/abs/2410.11637
This paper presents an explainable machine learning (ML) approach for predicting surface roughness in milling. Utilizing a dataset from milling aluminum alloy 2017A, the study employs random forest regression models and feature importance techniques.
Externí odkaz:
http://arxiv.org/abs/2409.10203
We analyse the impact of using tempered likelihoods in the production of posterior predictions. Our findings reveal that once the sample size is at least moderately large and the temperature is not too small, then likelihood tempering has virtually n
Externí odkaz:
http://arxiv.org/abs/2408.08806
Autor:
Knoblauch, Andreas
IVISIT is a generic interactive visual simulation tool that is based on Python/Numpy and can be used for system simulation, parameter optimization, parameter management, and visualization of system dynamics as required, for example,for developing neu
Externí odkaz:
http://arxiv.org/abs/2408.03341
Autor:
Duran-Martin, Gerardo, Altamirano, Matias, Shestopaloff, Alexander Y., Sánchez-Betancourt, Leandro, Knoblauch, Jeremias, Jones, Matt, Briol, François-Xavier, Murphy, Kevin
We derive a novel, provably robust, and closed-form Bayesian update rule for online filtering in state-space models in the presence of outliers and misspecified measurement models. Our method combines generalised Bayesian inference with filtering met
Externí odkaz:
http://arxiv.org/abs/2405.05646
In recent years, the shortcomings of Bayes posteriors as inferential devices has received increased attention. A popular strategy for fixing them has been to instead target a Gibbs measure based on losses that connect a parameter of interest to obser
Externí odkaz:
http://arxiv.org/abs/2404.15649
This research presents a method that utilizes explainability techniques to amplify the performance of machine learning (ML) models in forecasting the quality of milling processes, as demonstrated in this paper through a manufacturing use case. The me
Externí odkaz:
http://arxiv.org/abs/2403.18731
To enable closed form conditioning, a common assumption in Gaussian process (GP) regression is independent and identically distributed Gaussian observation noise. This strong and simplistic assumption is often violated in practice, which leads to unr
Externí odkaz:
http://arxiv.org/abs/2311.00463
Autor:
Ølberg, Judith Thu, Bohlinger, Patrik, Breivik, Øyvind, Christensen, Kai H., Furevik, Birgitte R., Hole, Lars R., Hope, Gaute, Jensen, Atle, Knoblauch, Fabian, Nguyen, Ngoc-Thanh, Rabault, Jean
This study reviews the design and signal processing of ship borne ultrasonic altimeter wave measurements. The system combines a downward facing ultrasonic altimeter to capture the sea surface elevation as a time series, and an inertial measurement un
Externí odkaz:
http://arxiv.org/abs/2310.03101
Autor:
Emmanuelle Sandra Adjoa Ametepe, Noela Andriamanoha, Fanantenana Randria Andrianomanana, Floriane Point, Reziky Tiandraza Mangahasimbola, Alina Dyachenko, Michael Hall, Theodora Mayouya Gamana, Astrid M. Knoblauch, Yemimah Yededyah Razafindrasoa, Arianminpathy Nimalan, Marcel Behr, Madeleine Durand, Mira Johri, Zamin Iqbal, Andry Rivo Rakotoarivelo, Rindra Vatosoa Randremanana, Niaina Rakotosamimanana, Simon Grandjean Lapierre
Publikováno v:
Trials, Vol 25, Iss 1, Pp 1-9 (2024)
Abstract Background Retrospective and descriptive molecular epidemiology studies have shown that Mycobacterium tuberculosis whole genome sequencing can identify outbreaks and disease transmission events with higher resolution than conventional epidem
Externí odkaz:
https://doaj.org/article/c507c6e54c2740afa7f02b50c7879650
