5122 search results for "git"

Predicting claims with a bayesian network

November 19, 2013
By
Predicting claims with a bayesian network

Here is a little Bayesian Network to predict the claims for two different types of drivers over the next year, see also example 16.16 in . Let's assume there are good and bad drivers. The probabilities that a good driver will have 0, 1 or 2 claims in any given year are set to 70%, 20% and 10%,...

Read more »

Towards the R package emdat: Losses from Global Disasters, Part 1

November 18, 2013
By

The International Disaster Database, EM-DAT from the Center for Research on the Epidemiology of Disasters (CRED, Belgium) is often used as a reference for losses on human life and property resulting from natural and man-made disasters. This databa...

Read more »

The homogenization of scientific computing, or why Python is steadily eating other languages’ lunch

November 18, 2013
By

Over the past two years, my scientific computing toolbox been steadily homogenizing. Around 2010 or 2011, my toolbox looked something like this: Ruby for text processing and miscellaneous scripting; Ruby on Rails/JavaScript for web development; Python/Numpy (mostly) and MATLAB (occasionally) for numerical computing; MATLAB for neuroimaging data analysis; R for statistical analysis; R for plotting

Read more »

Success rates for EPSRC Fellowships

November 18, 2013
By
Success rates for EPSRC Fellowships

Email I was recently at a presentation where the success rates for EPSRC fellowships were given by theme. The message of the talk was that Engineering fellowships were under-subscribed and so we should all be preparing our applications. But just because a theme is under-subcribed doesn’t mean that you’ve got a better chance of getting

Read more »

Historical Value at Risk versus historical Expected Shortfall

November 18, 2013
By
Historical Value at Risk versus historical Expected Shortfall

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...

Read more »

analyze the national survey of children’s health with r

November 18, 2013
By

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...

Read more »

Alpha testing shinyapps.io – first impressions

November 17, 2013
By
Alpha testing shinyapps.io – first impressions

ShinyApps.io is a new server which is currently in alpha testing to host Shiny applications.  It is being designed by the RStudio team and provides some distinct features different from that of the ShinyApps.io is intended for larger applications ...

Read more »

New R package sheldusr: Losses from Natural Disasters in the United States

November 17, 2013
By

The SHELDUS database is database on human and property losses from natural disasters in the United States. Although the data is free, downloading the data is tedious and so is cleaning and analyzing it. The new R package sheldusr comes w...

Read more »

Bayesian linear regression analysis without tears (R)

November 17, 2013
By
Bayesian linear regression analysis without tears (R)

Bayesian methods are sure to get some publicity after Vale Johnson’s PNAS paper regarding the use of Bayesian approaches to recalibrate p-value cutoffs from 0.05 to 0.005. Though the paper itself is bound to get some heat (see the discussion in Andrew Gelman’s blog and Matt Briggs’s fun-to-read deconstruction), the controversy might stimulate people to explore

Read more »

Linear Regression with R : step by step implementation part-2

November 16, 2013
By
Linear Regression with R : step by step implementation part-2

Welcome to the second part! In previous part, we understood Linear regression, cost function and gradient descent. In this part we will implement whole process in R step by step using example data set. I will use the data set provided in the machine learning class assignment. We will implement linear regression with one variable The post Linear...

Read more »