Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Victor M.-H. Ong"'
Publikováno v:
Foundations of Data Science. 1:129-156
Flexible density regression methods, in which the whole distribution of a response vector changes with the covariates, are very useful in some applications. A recently developed technique of this kind uses the matrix-variate Dirichlet process as a pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6a56c5b832a1e2cb9108cf1d9b2b04eb
https://doi.org/10.3934/fods.2019006
https://doi.org/10.3934/fods.2019006
Publikováno v:
Computational Statistics & Data Analysis. 128:271-291
One popular approach to likelihood-free inference is the synthetic likelihood method, which assumes that some data summary statistics which are informative about model parameters are approximately Gaussian for each value of the parameter. Based on th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6bc8e28b9eac1d5e6355451f275ee6de
https://doi.org/10.1016/j.csda.2018.07.008
https://doi.org/10.1016/j.csda.2018.07.008
Publikováno v:
Journal of Computational and Graphical Statistics. 27:465-478
Variational approximation methods have proven to be useful for scaling Bayesian computations to large data sets and highly parametrized models. Applying variational methods involves solving an optimization problem, and recent research in this area ha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b6e78f5b1110fafd3e10747e2ae707c1
https://doi.org/10.1080/10618600.2017.1390472
https://doi.org/10.1080/10618600.2017.1390472
Publikováno v:
Handbook of Approximate Bayesian Computation ISBN: 9781315117195
This Chapter, "High-dimensional ABC", is to appear in the forthcoming Handbook of Approximate Bayesian Computation (2018). It details the main ideas and concepts behind extending ABC methods to higher dimensions, with supporting examples and illustra
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https://explore.openaire.eu/search/publication?articleId=doi_________::5ff3d4f9478d04d78d95e6e4c77152b4
https://doi.org/10.1201/9781315117195-8
https://doi.org/10.1201/9781315117195-8
Publikováno v:
Electron. J. Statist. 11, no. 2 (2017), 4258-4296
This paper presents a variational Bayes approach to a semiparametric regression model that consists of parametric and nonparametric components. The assumed univariate nonparametric component is represented with a cosine series based on a spectral ana
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https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4d56578bfffe168ed121a683980e60c4
https://projecteuclid.org/euclid.ejs/1510111112
https://projecteuclid.org/euclid.ejs/1510111112
Synthetic likelihood is an attractive approach to likelihood-free inference when an approximately Gaussian summary statistic for the data, informative for inference about the parameters, is available. The synthetic likelihood method derives an approx
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https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f2790678eaaac21524dd8c6f9c932b49
http://arxiv.org/abs/1608.03069
http://arxiv.org/abs/1608.03069
We develop a fast variational approximation scheme for Gaussian process (GP) regression, where the spectrum of the covariance function is subjected to a sparse approximation. Our approach enables uncertainty in covariance function hyperparameters to
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https://explore.openaire.eu/search/publication?articleId=doi_dedup___::16cb50d23e01c671977fdb34df44e4ab
Publikováno v:
Scopus-Elsevier
androgens act through a single intracellular androgen receptor (AR) which is encoded by a single-copy gene in the X chromosome. Disruption of the AR by genetic mutation results in complete androgen insensitivity syndrome (CAIS) and the female phenoty
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https://explore.openaire.eu/search/publication?articleId=doi_dedup___::34a2ad357332e099598e871251c3c714
http://www.scopus.com/inward/record.url?eid=2-s2.0-0033974677&partnerID=MN8TOARS
http://www.scopus.com/inward/record.url?eid=2-s2.0-0033974677&partnerID=MN8TOARS