scholarly journals Streamlined mean field variational Bayes for longitudinal and multilevel data analysis

2016 ◽  
Vol 58 (4) ◽  
pp. 868-895 ◽  
Author(s):  
Cathy Yuen Yi Lee ◽  
Matt P. Wand
Keyword(s):  
Data Analysis ◽  
Mean Field ◽  
Variational Bayes ◽  
Multilevel Data

scholarly journals Modeling the health effects of time-varying complex environmental mixtures: Mean field variational Bayes for lagged kernel machine regression

2018 ◽  
Vol 29 (4) ◽  
pp. e2504 ◽  
Author(s):  
Shelley H. Liu ◽  
Jennifer F. Bobb ◽  
Birgit Claus Henn ◽  
Lourdes Schnaas ◽  
Martha M. Tellez-Rojo ◽  
...  
Keyword(s):  
Health Effects ◽  
Mean Field ◽  
Variational Bayes ◽  
Time Varying ◽  
Kernel Machine ◽  
Kernel Machine Regression ◽  
Environmental Mixtures

scholarly journals Topological data analysis and diagnostics of compressible magnetohydrodynamic turbulence

2018 ◽  
Vol 84 (4) ◽  
Author(s):  
I. Makarenko ◽  
P. Bushby ◽  
A. Fletcher ◽  
R. Henderson ◽  
N. Makarenko ◽  
...  
Keyword(s):  
Data Analysis ◽  
Random Fields ◽  
Large Scale ◽  
Betti Numbers ◽  
Mean Field ◽  
Topological Data Analysis ◽  
Topological Measures ◽  
Magnetohydrodynamic Simulations ◽  
Random Flows ◽  
Topological Data

The predictions of mean-field electrodynamics can now be probed using direct numerical simulations of random flows and magnetic fields. When modelling astrophysical magnetohydrodynamics, it is important to verify that such simulations are in agreement with observations. One of the main challenges in this area is to identify robust quantitative measures to compare structures found in simulations with those inferred from astrophysical observations. A similar challenge is to compare quantitatively results from different simulations. Topological data analysis offers a range of techniques, including the Betti numbers and persistence diagrams, that can be used to facilitate such a comparison. After describing these tools, we first apply them to synthetic random fields and demonstrate that, when the data are standardized in a straightforward manner, some topological measures are insensitive to either large-scale trends or the resolution of the data. Focusing upon one particular astrophysical example, we apply topological data analysis to H iobservations of the turbulent interstellar medium (ISM) in the Milky Way and to recent magnetohydrodynamic simulations of the random, strongly compressible ISM. We stress that these topological techniques are generic and could be applied to any complex, multi-dimensional random field.

scholarly journals Mean Field Variational Bayes for Elaborate Distributions

2011 ◽  
Vol 6 (4) ◽  
pp. 847-900 ◽  
Author(s):  
Matthew P. Wand ◽  
John T. Ormerod ◽  
Simone A. Padoan ◽  
Rudolf Frühwirth
Keyword(s):  
Mean Field ◽  
Variational Bayes

scholarly journals Mean field variational Bayes for continuous sparse signal shrinkage: Pitfalls and remedies

2014 ◽  
Vol 8 (1) ◽  
pp. 1113-1151 ◽  
Author(s):  
Sarah E. Neville ◽  
John T. Ormerod ◽  
M. P. Wand
Keyword(s):  
Mean Field ◽  
Variational Bayes ◽  
Sparse Signal

Statistical Procedures for Spatiotemporal Neuronal Data with Applications to Optical Recording of the Auditory Cortex

2000 ◽  
Vol 12 (8) ◽  
pp. 1821-1838 ◽  
Author(s):  
O. François ◽  
L. Mohamed Abdallahi ◽  
J. Horikawa ◽  
I. Taniguchi ◽  
T. Hervé
Keyword(s):  
Experimental Data ◽  
Data Analysis ◽  
Auditory Cortex ◽  
Mean Field ◽  
Innovation Process ◽  
Optical Recording ◽  
Internal Process ◽  
Statistical Procedures ◽  
New Procedures ◽  
Mean Field Approximations

This article presents new procedures for multisite spatiotemporal neuronal data analysis. A new statistical model—the diffusion model—is considered, whose parameters can be estimated from experimental data thanks to mean-field approximations. This work has been applied to optical recording of the guinea pig's auditory cortex (layers II—III). The rates of innovation and internal diffusion inside the stimulated area have been estimated. The results suggest that the activity of the layer balances between the alternate predominance of its innovation process and its internal process.

scholarly journals Multilevel data analysis: What? Why? How?

2018 ◽  
Vol 155 (1) ◽  
pp. 210-211 ◽  
Author(s):  
Jeevanantham Rajeswaran ◽  
Eugene H. Blackstone
Keyword(s):  
Data Analysis ◽  
Multilevel Data
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