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pro vyhledávání: '"boom, willem"'
We propose an adaptive importance sampling scheme for Gaussian approximations of intractable posteriors. Optimization-based approximations like variational inference can be too inaccurate while existing Monte Carlo methods can be too slow. Therefore,
Externí odkaz:
http://arxiv.org/abs/2404.18556
High-dimensional data analysis typically focuses on low-dimensional structure, often to aid interpretation and computational efficiency. Graphical models provide a powerful methodology for learning the conditional independence structure in multivaria
Externí odkaz:
http://arxiv.org/abs/2310.11741
Publikováno v:
Biometrics 80 (2024) ujae075
Time-to-event data are often recorded on a discrete scale with multiple, competing risks as potential causes for the event. In this context, application of continuous survival analysis methods with a single risk suffer from biased estimation. Therefo
Externí odkaz:
http://arxiv.org/abs/2308.10583
Publikováno v:
Philosophical Transactions of the Royal Society A 381 (2023) 20220145
Several applications involving counts present a large proportion of zeros (excess-of-zeros data). A popular model for such data is the Hurdle model, which explicitly models the probability of a zero count, while assuming a sampling distribution on th
Externí odkaz:
http://arxiv.org/abs/2205.05054
Publikováno v:
Journal of Applied Probability 61 (2024) 230-243
Gaussian graphical models are useful tools for conditional independence structure inference of multivariate random variables. Unfortunately, Bayesian inference of latent graph structures is challenging due to exponential growth of $\mathcal{G}_n$, th
Externí odkaz:
http://arxiv.org/abs/2205.04324
Autor:
Cremaschi, Andrea, van den Boom, Willem, Ng, Nicholas Beng Hui, Franzolini, Beatrice, Tan, Kelvin B., Chan, Jerry Kok Yen, Tan, Kok Hian, Chong, Yap-Seng, Eriksson, Johan G., De Iorio, Maria
Publikováno v:
In Value in Health Regional Issues January 2025 45
