Monthly Archives: October 2011

Approximate Bayesian computational methods on-line

October 25, 2011
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Approximate Bayesian computational methods on-line

Fig. 4 – Boxplots of the evolution of ABC approximations to the Bayes factor. The representation is made in terms of frequencies of visits to models MA(1) and MA(2) during an ABC simulation when ε corresponds to the 10,1,.1,.01% quantiles on the simulated autocovariance distances. The data is a time

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Machine Learning Ex 5.1 – Regularized Linear Regression

October 25, 2011
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Machine Learning Ex 5.1 – Regularized Linear Regression

The first part of the Exercise 5.1 requires to implement a regularized version of linear regression. Adding regularization parameter can prevent the problem of over-fitting when fitting a high-order polynomial. Read More: 194 Words Totally

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Vanilla C code for the Stochastic Simulation Algorithm

October 24, 2011
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Vanilla C code for the Stochastic Simulation Algorithm

The Gillespie stochastic simulation algorithm (SSA) is the gold standard for simulating state-based stochastic models. If you are a R buff, a SSA novice and want to get quickly up and running stochastic models (in particular ecological models) that are not … Continue reading →

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Simple Heatmap in R with Formula One Dataset

October 24, 2011
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Simple Heatmap in R with Formula One Dataset

Now, that the 2011 F1 season is over I decided to quickly scrub the Formula 1 data of the F1.com website, such as the list of drivers, ordered by the approximate amount of salary driver is getting (top list driver is making the most, approx. 30MM) and position at the end of each race. There

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One week left to enter the $20,000 "Applications of R" contest

October 24, 2011
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One week left to enter the $20,000 "Applications of R" contest

The deadline to enter the "Applications of R in Business" contest is just a week away. To qualify for $20,000 in prizes from Revolution Analytics, your entry must be submitted to inside-r.org by midnight PST on October 31. Note that this doesn't have to be your final submission: as long as you've entered a draft version, you can still...

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Two seasonal investors – R snippet

October 24, 2011
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Two seasonal investors – R snippet

In “A tale of 2 Seasonal Investors“, the Big Picture discusses the simple idea of comparing two simple investment approaches: being exposed to the market 6 months every year (from November to April), as opposed to investing in the other 6 months of every year (from May to October). Going back 50 years in the

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NYT on Big Data and R

October 24, 2011
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In the New York Times' "Bits" blog today, Quentin Hardy offers recollections on Big Data talks at the recent Web 2.0 Summit. He begins with a definition of Big Data: Big Data is really about ... the benefits we will gain by cleverly sifting through it to find and exploit new patterns and relationships. You see it now in...

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Show me your WAR face!

October 24, 2011
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Show me your WAR face!

Below is a chart of the top 20 offensive players based on FanGraphs WAR for the 2011 season.  The various features and their corresponding metric are clear in the image. I’ve also included the leader and last place for each … Continue reading →

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XLConnect 0.1-7

October 24, 2011
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XLConnect 0.1-7

Mirai Solutions GmbH (http://www.mirai-solutions.com) is pleased to announce the availability of XLConnect 0.1-7. This release includes a number of improvements and new features: Performance improvements when writing large xlsx files New workbook data extraction & replacement operators [, [<-, [[, … Continue reading →

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Parameter vs. Observation Dimension?

October 24, 2011
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Parameter vs. Observation Dimension?

Bill Bolstad's response to Xi'an's review of his book Understanding Computational Bayesian Statistics included the following comment, which I found interesting: Frequentist p-values are constructed in the parameter dimension using a probability distribution defined only in the observation dimension. Bayesian credible intervals are constructed in the parameter dimension using a probability distribution in the parameter

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