Existence of Curves of Maximal Slope and their Variational Approximation

2008 ◽  
pp. 39-57
Keyword(s):  
Variational Approximation

Manifolds in random media: beyond the variational approximation

1994 ◽  
Vol 4 (1) ◽  
pp. 87-100 ◽  
Author(s):  
Yadin Y. Goldschmidt
Keyword(s):  
Random Media ◽  
Variational Approximation

Identifying graph clusters using variational inference and links to covariance parametrization

2009 ◽  
Vol 367 (1906) ◽  
pp. 4407-4426
Author(s):  
David Barber
Keyword(s):  
Mean Field ◽  
Matrix Decomposition ◽  
Difficult Problem ◽  
Positive Definite ◽  
Variational Inference ◽  
Field Theories ◽  
Variational Approximation ◽  
Positive Definite Matrices ◽  
The Matrix ◽  
Non Gaussian

Finding clusters of well-connected nodes in a graph is a problem common to many domains, including social networks, the Internet and bioinformatics. From a computational viewpoint, finding these clusters or graph communities is a difficult problem. We use a clique matrix decomposition based on a statistical description that encourages clusters to be well connected and few in number. The formal intractability of inferring the clusters is addressed using a variational approximation inspired by mean-field theories in statistical mechanics. Clique matrices also play a natural role in parametrizing positive definite matrices under zero constraints on elements of the matrix. We show that clique matrices can parametrize all positive definite matrices restricted according to a decomposable graph and form a structured factor analysis approximation in the non-decomposable case. Extensions to conjugate Bayesian covariance priors and more general non-Gaussian independence models are briefly discussed.

scholarly journals A simple new approach to variable selection in regression, with application to genetic fine-mapping

2018 ◽  
Author(s):  
Gao Wang ◽  
Abhishek Sarkar ◽  
Peter Carbonetto ◽  
Matthew Stephens
Keyword(s):  
Variable Selection ◽  
Fine Mapping ◽  
Posterior Distribution ◽  
Zero Element ◽  
Variational Approximation ◽  
New Approach ◽  
Stepwise Selection ◽  
Fitting Procedure ◽  
Highly Correlated ◽  
Credible Set

We introduce a simple new approach to variable selection in linear regression, with a particular focus on quantifying uncertainty in which variables should be selected. The approach is based on a new model — the “Sum of Single Effects” (SuSiE) model — which comes from writing the sparse vector of regression coefficients as a sum of “single-effect” vectors, each with one non-zero element. We also introduce a corresponding new fitting procedure — Iterative Bayesian Stepwise Selection (IBSS) — which is a Bayesian analogue of stepwise selection methods. IBSS shares the computational simplicity and speed of traditional stepwise methods, but instead of selecting a single variable at each step, IBSS computes a distribution on variables that captures uncertainty in which variable to select. We provide a formal justification of this intuitive algorithm by showing that it optimizes a variational approximation to the posterior distribution under the SuSiE model. Further, this approximate posterior distribution naturally yields convenient novel summaries of uncertainty in variable selection, providing a Credible Set of variables for each selection. Our methods are particularly well-suited to settings where variables are highly correlated and detectable effects are sparse, both of which are characteristics of genetic fine-mapping applications. We demonstrate through numerical experiments that our methods outper-form existing methods for this task, and illustrate their application to fine-mapping genetic variants influencing alternative splicing in human cell-lines. We also discuss the potential and challenges for applying these methods to generic variable selection problems.

PARAMETRIC EXCITATION OF SOLITONS IN DIPOLAR BOSE–EINSTEIN CONDENSATES

2013 ◽  
Vol 27 (25) ◽  
pp. 1350184 ◽  
Author(s):  
A. BENSEGHIR ◽  
W. A. T. WAN ABDULLAH ◽  
B. A. UMAROV ◽  
B. B. BAIZAKOV
Keyword(s):  
Parametric Excitation ◽  
Einstein Condensate ◽  
Quantum Gases ◽  
Variational Approximation ◽  
Pitaevskii Equation ◽  
Nonlocal Nonlinearity ◽  
Bose Einstein Condensate ◽  
Atomic Interactions ◽  
Theoretical Predictions ◽  
Bose Einstein

In this paper, we study the response of a Bose–Einstein condensate with strong dipole–dipole atomic interactions to periodically varying perturbation. The dynamics is governed by the Gross–Pitaevskii equation with additional nonlinear term, corresponding to a nonlocal dipolar interactions. The mathematical model, based on the variational approximation, has been developed and applied to parametric excitation of the condensate due to periodically varying coefficient of nonlocal nonlinearity. The model predicts the waveform of solitons in dipolar condensates and describes their small amplitude dynamics quite accurately. Theoretical predictions are verified by numerical simulations of the nonlocal Gross–Pitaevskii equation and good agreement between them is found. The results can lead to better understanding of the properties of ultra-cold quantum gases, such as 52 Cr , 164 Dy and 168 Er , where the long-range dipolar atomic interactions dominate the usual contact interactions.

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