**O**n Friday, a 2008 paper on *Stochastic Approximation and Newton’s Estimate of a Mixing Distribution* by Ryan Martin and J.K. Ghosh was posted on arXiv. (I do not really see why it took so long to post on arXiv a 2008 *Statistical Science* paper but given that it is not available on project Euclid, it may be that not all papers in *Statistical Science *are published immediately. Anyway, this is irrelevant to my point here!).

The paper provides a very nice introduction to stochastic approximation methods, making the link to recent works by Christophe Andrieu, Heikki Haario, Faming Liang, Eric Moulines, Enro Saksman, and co-authors. Martin and Ghosh also reinterpret Newton-Raphson as a special case of stochastic approximation. The whole paper is very pleasant to read, quite in tune with *Statistical Science*. I will most certainly use this material in my graduate courses and also include part of it in the revision of **Monte Carlo Statistical Methods**.

Filed under: R, Statistics Tagged: J.K. Ghosh, mixtures, Newton-Raphson, Robbins-Monro, Statistical Science, stochastic approximation

*Related*

To

**leave a comment** for the author, please follow the link and comment on their blog:

** Xi'an's Og » R**.

R-bloggers.com offers

**daily e-mail updates** about

R news and

tutorials on topics such as:

Data science,

Big Data, R jobs, visualization (

ggplot2,

Boxplots,

maps,

animation), programming (

RStudio,

Sweave,

LaTeX,

SQL,

Eclipse,

git,

hadoop,

Web Scraping) statistics (

regression,

PCA,

time series,

trading) and more...

If you got this far, why not

__subscribe for updates__ from the site? Choose your flavor:

e-mail,

twitter,

RSS, or

facebook...

**Tags:** J.K. Ghosh, mixtures, Newton-Raphson, R, Robbins-Monro, Statistical Science, statistics, stochastic approximation