The variational approximation for Bayesian inference Export

@article{citeulike:4027355, abstract = {The influence of this Thomas Bayes' work was immense. It was from here that "Bayesian" ideas first spread through the mathematical world, as Bayes's own article was ignored until 1780 and played no important role in scientific debate until the 20th century. It was also this article of Laplace's that introduced the mathematical techniques for the asymptotic analysis of posterior distributions that are still employed today. And it was here that the earliest example of optimum estimation can be found, the derivation and characterization of an estimator that minimized a particular measure of posterior expected loss. After more than two centuries, we mathematicians, statisticians cannot only recognize our roots in this masterpiece of our science, we can still learn from it.}, author = {Tzikas, D. G. and Likas, A. C. and Galatsanos, N. P.}, booktitle = {Signal Processing Magazine, IEEE}, citeulike-article-id = {4027355}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/MSP.2008.929620}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4644060}, doi = {10.1109/MSP.2008.929620}, journal = {Signal Processing Magazine, IEEE}, keywords = {bayesian, em, machine-learning}, number = {6}, pages = {131--146}, posted-at = {2009-02-16 20:35:58}, priority = {2}, title = {The variational approximation for Bayesian inference}, url = {http://dx.doi.org/10.1109/MSP.2008.929620}, volume = {25}, year = {2008} }

TY - JOUR ID - citeulike:4027355 L3 - citeulike-article-id:4027355 N2 - The influence of this Thomas Bayes' work was immense. It was from here that "Bayesian" ideas first spread through the mathematical world, as Bayes's own article was ignored until 1780 and played no important role in scientific debate until the 20th century. It was also this article of Laplace's that introduced the mathematical techniques for the asymptotic analysis of posterior distributions that are still employed today. And it was here that the earliest example of optimum estimation can be found, the derivation and characterization of an estimator that minimized a particular measure of posterior expected loss. After more than two centuries, we mathematicians, statisticians cannot only recognize our roots in this masterpiece of our science, we can still learn from it. IS - 6 JF - Signal Processing Magazine, IEEE EP - 146 TI - The variational approximation for Bayesian inference VL - 25 T2 - Signal Processing Magazine, IEEE SP - 131 KW - bayesian KW - em KW - machine-learning AU - Tzikas, DG AU - Likas, AC AU - Galatsanos, NP PY - 2008/// UR - http://dx.doi.org/10.1109/MSP.2008.929620 ER -