A study is made of the simple empirical Bayes estimators proposed by Robbins (1956). These estimators are compared with `best' conventional estimators in terms of ...

The Canadian Journal of Statistics / La Revue Canadienne de Statistique, Vol. 46, No. 3 (September/septembre 2018), pp. 399-415 (17 pages) For sparse and high-dimensional data analysis, a valid ...

Bayesian estimation and maximum likelihood methods represent two central paradigms in modern statistical inference. Bayesian estimation incorporates prior beliefs through Bayes’ theorem, updating ...

Over the years, many writers have implied that statistics can provide almost any result that is convenient at the time. Of course, honest practitioners use statistics in an attempt to quantify the ...

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and ...

Bayesian networks, also known as Bayes nets, belief networks, or decision networks, are a powerful tool for understanding and reasoning about complex systems under uncertainty. They are essentially ...

In science, progress is possible. In fact, if one believes in Bayes' theorem, scientific progress is inevitable as predictions are made and as beliefs are tested and refined. ~ Nate Silver If the ...