Zobrazeno 1 - 10
of 109
pro vyhledávání: '"Wand, MP"'
We explain how effective automatic probability density function estimates can be constructed using contemporary Bayesian inference engines such as those based on no-U-turn sampling and expectation propagation. Extensive simulation studies demonstrate
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______363::32585589f34d2a726724229545eee169
https://hdl.handle.net/10453/166309
https://hdl.handle.net/10453/166309
Autor:
Maestrini L, Wand MP
Message passing on a factor graph is a powerful paradigm for the coding of approximate inference algorithms for arbitrarily graphical large models. The notion of a factor graph fragment allows for compartmentalization of algebra and computer code. We
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______363::fde1761ac5a51249061f54d6b3bd21a8
https://hdl.handle.net/10453/144508
https://hdl.handle.net/10453/144508
© 2020 Statistical Modeling Society. A two-level group-specific curve model is such that the mean response of each member of a group is a separate smooth function of a predictor of interest. The three-level extension is such that one grouping variab
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1943dfaf74500b9fd471c05301a3f2f3
http://arxiv.org/abs/1903.04043
http://arxiv.org/abs/1903.04043
Copyright © 2017 John Wiley & Sons, Ltd. We provide full algebraic and numerical details required for fitting accurate logistic likelihood regression-type models via variational message passing with factor graph fragments. Existing methodology of th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______363::73c9f97a3d561ce930498ed58b3b01a9
https://hdl.handle.net/10453/111915
https://hdl.handle.net/10453/111915
© 2016, Institute of Mathematical Statistics. All rights reserved. We derive the explicit form of expectation propagation for approximate deterministic Bayesian inference in a simple statistical model. The model corresponds to a random sample from t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______363::c39bae68bef9d2f4deb595d03b1e7588
https://hdl.handle.net/10453/43963
https://hdl.handle.net/10453/43963
Autor:
Wand, MP
Fully simplified expressions for Multivariate Normal updates in non-conjugate variational message passing approximate inference schemes are obtained. The simplicity of these expressions means that the updates can be achieved very eficiently. Since th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______363::64f09b2c8720eeddb6f1291ea77bd381
https://hdl.handle.net/10453/41190
https://hdl.handle.net/10453/41190
Autor:
Menictas, M, Wand, MP
© 2013 John Wiley & Sons Ltd. We derive a variational inference procedure for approximate Bayesian inference in marginal longitudinal semiparametric regression. Fitting and inference is much faster than existing Markov chain Monte Carlo approaches.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______363::931737a09bf4424cf6de09e5769854d6
https://hdl.handle.net/10453/26440
https://hdl.handle.net/10453/26440
Autor:
Wand, MP, Ormerod, JT
© 2012 John Wiley & Sons, Ltd. The agéd number theoretic concept of continued fractions can enhance certain Bayesian computations. The crux of this claim is due to continued fraction representations of numerically challenging special function ratio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______363::58377aa6c10efb09d81a8eb20a2b67bb
https://hdl.handle.net/10453/22024
https://hdl.handle.net/10453/22024
We demonstrate and critique the new Bayesian inference package Infer.NET in terms of its capacity for statistical analyses. Infer.NET differs from the well-known BUGS Bayesian inference packages in that its main engine is the variational Bayes family
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______363::5a8a2bc070be9cec5f247c45e0475d27
https://hdl.handle.net/10453/17929
https://hdl.handle.net/10453/17929