Journal ArticleDOI
Variational inference for marginal longitudinal semiparametric regression
Marianne Menictas,Matt P. Wand +1 more
- Vol. 2, Iss: 1, pp 61-71
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TLDR
In this paper, a variational inference procedure for approximate Bayesian inference in marginal longitudinal semiparametric regression is presented, which is much faster than existing Markov chain Monte Carlo approaches.Abstract:
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. Numerical studies indicate that the new methodology is very accurate for the class of models under consideration. Copyright © 2013 John Wiley & Sons Ltdread more
Citations
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Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Journal ArticleDOI
Simple Marginally Noninformative Prior Distributions for Covariance Matrices
Alan Huang,Matt P. Wand +1 more
TL;DR: In this paper, a family of prior distributions for covariance matrices is studied, which possess the attractive property of all standard deviation and correlation parameters being marginally noninformative for particular hyper-parameter choices.
Journal ArticleDOI
Real-Time Semiparametric Regression
TL;DR: In this article, the authors develop algorithms for performing semiparametric regression analysis in real time, with data processed as it is collected and made immediately available via modern telecommunications technologies, and demonstrate the methodology for continually arriving stock market, real estate, and airline data.
Journal ArticleDOI
Streamlined mean field variational Bayes for longitudinal and multilevel data analysis.
Cathy Yuen Yi Lee,Matt P. Wand +1 more
TL;DR: Streamlined mean field variational Bayes algorithms for efficient fitting and inference in large models for longitudinal and multilevel data analysis are obtained, allowing the fastest ever approximate Bayesian analyses of arbitrarily large longitudinal andMultilevel datasets.
Posted Content
Real-time semiparametric regression
TL;DR: In this paper, the authors develop algorithms for performing semiparametric regression analysis in real time, with data processed as it is collected and made immediately available via modern telecommunications technologies, and demonstrate the methodology for continually arriving stock market, real estate and airline data.
References
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Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Journal ArticleDOI
That BLUP is a Good Thing: The Estimation of Random Effects
TL;DR: In animal breeding, Best Linear Unbiased Prediction (BLUP) as mentioned in this paper is a technique for estimating genetic merits, which can be used to derive the Kalman filter, the method of Kriging used for ore reserve estimation, credibility theory used to work out insurance premiums, and Hoadley's quality measurement plan used to estimate a quality index.
Journal ArticleDOI
Explaining Variational Approximations
John T. Ormerod,Matt P. Wand +1 more
TL;DR: Variational approximations facilitate approximate inference for the parameters in complex statistical models and provide fast, deterministic alternatives to Monte Carlo methods as discussed by the authors, however, much of the contemporary literature on variational approximation is in Computer Science rather than Statistics, and uses terminology, notation, and examples from the former field.
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