Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Märtens, Kaspar"'
Autor:
Märtens, Kaspar, Yau, Christopher
Generative models for multimodal data permit the identification of latent factors that may be associated with important determinants of observed data heterogeneity. Common or shared factors could be important for explaining variation across modalitie
Externí odkaz:
http://arxiv.org/abs/2403.06338
We introduce Markov Neural Processes (MNPs), a new class of Stochastic Processes (SPs) which are constructed by stacking sequences of neural parameterised Markov transition operators in function space. We prove that these Markov transition operators
Externí odkaz:
http://arxiv.org/abs/2305.15574
Autor:
Märtens, Kaspar, Yau, Christopher
Publikováno v:
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 108:2917-2927, 2020
Variational Autoencoders (VAEs) have become a popular approach for dimensionality reduction. However, despite their ability to identify latent low-dimensional structures embedded within high-dimensional data, these latent representations are typicall
Externí odkaz:
http://arxiv.org/abs/2006.14293
Autor:
Märtens, Kaspar, Yau, Christopher
Variational Autoencoders (VAEs) provide a flexible and scalable framework for non-linear dimensionality reduction. However, in application domains such as genomics where data sets are typically tabular and high-dimensional, a black-box approach to di
Externí odkaz:
http://arxiv.org/abs/2003.03462
Publikováno v:
Proceedings of the 36th International Conference on Machine Learning (ICML 2019)
The interpretation of complex high-dimensional data typically requires the use of dimensionality reduction techniques to extract explanatory low-dimensional representations. However, in many real-world problems these representations may not be suffic
Externí odkaz:
http://arxiv.org/abs/1810.06983
Publikováno v:
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS) 2019, Naha,Okinawa, Japan. PMLR: Volume 89
Bayesian inference for factorial hidden Markov models is challenging due to the exponentially sized latent variable space. Standard Monte Carlo samplers can have difficulties effectively exploring the posterior landscape and are often restricted to e
Externí odkaz:
http://arxiv.org/abs/1703.08520
Akademický článek
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Autor:
Elison-Davies, Sarah1 (AUTHOR) selison@breakingfreegroup.com, Märtens, Kaspar2 (AUTHOR), Yau, Christopher3 (AUTHOR), Davies, Glyn1 (AUTHOR), Ward, Jonathan1 (AUTHOR)
Publikováno v:
American Journal of Drug & Alcohol Abuse. 2021, Vol. 47 Issue 3, p360-372. 13p.
Autor:
van de Schoot, R., Depaoli, Sarah, Kramer, Bianca, Märtens, Kaspar, Tadesse, Mahlet G., Vannucci, Marina, Gelman, Andrew, Veen, Duco, Willemsen, Joukje, Yau, Christopher
Publikováno v:
van de Schoot, R, Depaoli, S, Gelman, A, King, R, Kramer, B, Märtens, K, Tadesse, M G, Vannucci, M, Willemsen, J & Yau, C 2021, ' Bayesian statistics and modelling ', Nature Reviews Methods Primers, vol. 1, 3 . https://doi.org/10.1038/s43586-020-00003-0
Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowledge about parameters in a statistical model is updated with the information in observed data. The background knowledge is expressed as a prior distri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::87a2236f7b77c0e2eca5b9eb93295db2
https://www.pure.ed.ac.uk/ws/files/173819019/Bayesian_stats_revision_v06_submitted.pdf
https://www.pure.ed.ac.uk/ws/files/173819019/Bayesian_stats_revision_v06_submitted.pdf
Autor:
Lokk, Kaie, Vijayachitra Modhukur, Balaji Rajashekar, Märtens, Kaspar, Reedik Mägi, Kolde, Raivo, Koltšina, Marina, Nilsson, Torbjörn, Vilo, Jaak, Salumets, Andres, Tönisson, Neeme
Methylation calidation using Sanger sequencing. For validation of the methylation data from BeadChip, 17 genes were chosen, including unmethylated sites (n = 1), fully methylated sites (n = 2), and genes with tDMRs (n = 14) representing 36 CpG sites
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2f71397bbcf3d21f376e48de0bd745b5