1779 search results for "time series"

Shaping up Laplace Approximation using Importance Sampling

December 2, 2013
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Shaping up Laplace Approximation using Importance Sampling

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...

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Analyzing baseball data with R

November 27, 2013
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Analyzing baseball data with R

This week, the post is an interview with Max Marchi. Max is the author, with Jim Albert, of the book "Analyzing baseball data with R". Hi, Max. Welcome back to MilanoR. Last time you wrote for us a series of … Continue reading →

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The R Backpages 2

November 27, 2013
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The R Backpages 2

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...

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Five ways to handle Big Data in R

November 27, 2013
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Five ways to handle Big Data in R

Big data was one of the biggest topics on this year’s useR conference in Albacete and it is definitely one of today’s hottest buzzwords. But what defines “Big Data”? And on the practical side: How can big data be tackled in R? What data is big? Hadley Wickham, one of the best known R developers,

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From area under the curve to the fundamental theorem of calculus

November 24, 2013
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From area under the curve to the fundamental theorem of calculus

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

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Easy Laplace Approximation of Bayesian Models in R

November 21, 2013
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Easy Laplace Approximation of Bayesian Models in R

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...

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Plugging hierarchical data from R into d3

November 20, 2013
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Plugging hierarchical data from R into d3

Here I show how to convert tabulated data into a json format that can be used in d3 graphics. The motivation for this was an attempt at getting an overview of topic models (link). Illustrations like the one to the right are very attractive; my motivati...

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Historical Value at Risk versus historical Expected Shortfall

November 18, 2013
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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...

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analyze the national survey of children’s health with r

November 18, 2013
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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...

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Ensemble Methods, part 1

November 17, 2013
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Ensemble Methods, part 1

Last week I dabbled in building classification trees with the party and rpart packages.  Now, I want to put together a series where I can apply those basic trees along with advanced techniques like bagging, boosting and random forest.  Additi...

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