This is a lecture post for my students in the CUNY MS Data Analytics program. In this series of lectures …Continue reading »

In the last post I showed how to use Laplace approximation to quickly (but dirtily) approximate the posterior distribution of a Bayesian model coded in R. This is just a short follow up where I show how to use importance sampling as an easy method to shape up the Laplace approximation in order to approximate the true...

by Joseph Rickert In this roundup of R-related news: Domino enables data science collaboration; Plotly adds an R graphics gallery; Revolution Analytics R user group sponsorship applications are open; and Quandl adds new data sets. San Francisco startup takes on collaborative Data Science Domino, a San Francisco based startup, is inviting users to sign up to beta test its...

Thank you for tuning in! In this post, a continuation of Three Ways to Run Bayesian Models in R, I will: Handwave an explanation of the Laplace Approximation, a fast and (hopefully not too) dirty method to approximate the posterior of a Bayesian model. Show that it is super easy to do Laplace approximation in R, basically four...

Comparing the behavior of the two on the S&P 500. Previously There have been a few posts about Value at Risk (VaR) and Expected Shortfall (ES) including an introduction to Value at Risk and Expected Shortfall. Data and model The underlying data are daily returns for the S&P 500 from 1950 to the present. The VaR and … Continue reading...

american children of the nineties might have had pogs, beanie babies, m.c. hammer, but we lacked a reliable source for state-level survey estimates on health. then in 2003, the maternal and child health bureau of the health services and resources...