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pro vyhledávání: '"Wilson, Simon P."'
Implementation of many statistical methods for large, multivariate data sets requires one to solve a linear system that, depending on the method, is of the dimension of the number of observations or each individual data vector. This is often the limi
Externí odkaz:
http://arxiv.org/abs/2204.08057
This work uses hierarchical logistic Gaussian processes to infer true redshift distributions of samples of galaxies, through their cross-correlations with spatially overlapping spectroscopic samples. We demonstrate that this method can accurately est
Externí odkaz:
http://arxiv.org/abs/1904.09988
A method for conducting Bayesian elicitation and learning in risk assessment is presented. It assumes that the risk process can be described as a fault tree. This is viewed as a belief network, for which prior distributions on primary event probabili
Externí odkaz:
http://arxiv.org/abs/1904.03012
In this paper we present a Bayesian competing risk proportional hazards model to describe mortgage defaults and prepayments. We develop Bayesian inference for the model using Markov chain Monte Carlo methods. Implementation of the model is illustrate
Externí odkaz:
http://arxiv.org/abs/1706.07677
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Akademický článek
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Autor:
Wilson, Simon
Publikováno v:
Medico-Legal Journal; September 2024, Vol. 92 Issue: 3 p117-122, 6p
Autor:
Mai, Tiep, Wilson, Simon
A method for sequential inference of the fixed parameters of a dynamic latent Gaussian models is proposed and evaluated that is based on the iterated Laplace approximation. The method provides a useful trade-off between computational performance and
Externí odkaz:
http://arxiv.org/abs/1509.07900
Autor:
Mai, Tiep, Wilson, Simon
In this paper, several modifications are introduced to the functional approximation method iterLap to reduce the approximation error, including stopping rule adjustment, proposal of new residual function, starting point selection for numerical optimi
Externí odkaz:
http://arxiv.org/abs/1509.06492
Autor:
Bhattacharya, Arnab, Wilson, Simon
A method for sequential Bayesian inference of the static parameters of a dynamic state space model is proposed. The method is based on the observation that many dynamic state space models have a relatively small number of static parameters (or hyper-
Externí odkaz:
http://arxiv.org/abs/1408.4559