Academic Journal

Author manuscript, published in '32nd International Workshop on Bayesian Inference and Maximun Entropy Methods in Sciences and Engineerin, Garching near Munich: Germany (2012)' Variational Bayesian Approximation with scale

التفاصيل البيبلوغرافية
العنوان: Author manuscript, published in '32nd International Workshop on Bayesian Inference and Maximun Entropy Methods in Sciences and Engineerin, Garching near Munich: Germany (2012)' Variational Bayesian Approximation with scale
المؤلفون: Leila Gharsalli, Ali Mohammad-djafari, Aurélia Fraysse, Thomas Rodet
المساهمون: The Pennsylvania State University CiteSeerX Archives
المصدر: http://hal.inria.fr/docs/00/85/47/83/PDF/MaxEnt2012_Leila_Gharsalli.pdf.
سنة النشر: 2013
المجموعة: CiteSeerX
الوصف: Our aim is to solve a linear inverse problem using various methods based on the Variational Bayesian Approximation (VBA). We choose to take sparsity into account via a scale mixture prior, more precisely a student-t model. The joint posterior of the unknown and hidden variable of the mixtures is approximated via the VBA. To do this approximation, classically the alternate algorithm is used. But this method is not the most efficient. Recently other optimization algorithms have been proposed; indeed classical iterative algorithms of optimization such as the steepest descent method and the conjugate gradient have been studied in the space of the probability densities involved in the Bayesian methodology to treat this problem. The main object of this work is to present these three algorithms and a numerical comparison of their performances.
نوع الوثيقة: text
وصف الملف: application/pdf
اللغة: English
Relation: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.410.8598; http://hal.inria.fr/docs/00/85/47/83/PDF/MaxEnt2012_Leila_Gharsalli.pdf
الاتاحة: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.410.8598
http://hal.inria.fr/docs/00/85/47/83/PDF/MaxEnt2012_Leila_Gharsalli.pdf
Rights: Metadata may be used without restrictions as long as the oai identifier remains attached to it.
رقم الانضمام: edsbas.911F34D7
قاعدة البيانات: BASE