Win-Vector LLC’s Nina Zumel takes some time off to publish a literary book review: Reading Red Spectres: Russian Gothic Tales.

Nina Zumel also examines aspects of the supernatural in literature and in folk culture at her blog, multoghost.wordpress.com. She writes about folklore, ghost stories, weird fiction, or anything else that strikes her fancy. Follow her on Twitter @multoghost.

Manning Publications Inc. is launching an exciting new MEAP: Practical Probabilistic Programming (which we have already subscribed to) by offering a 50% discount on Practical Probabilistic Programming and other titles (including Practical Data Science with R!). To get the discount put the books in your Manning shopping car and then add the promotional code **ppplaunch50** (through May 30, 2014) into the coupon code field in the “other” section on towards the bottom of the account form. See below for other Manning books eligible for this generous discount. Read more…

Please share this generous deal from Manning publications: save 45% on Practical Data Science with R through May 21, 2014. Please tweet, forward and share!

Edit: we are going to try and keep the current best deals on the book at the bottom of the

Practical Data Science with R page. So look there for updates (also the book is always available at

Amazon.com so you may want to look what the discount there is).

Found this great offer from mkt@manning.com in our email today! Very excited to see Nina Zumel get some recognition and thought we would share it (and the generous discount) here. Read more…

It took a little longer than we’d hoped, but we did it! *Practical Data Science with R* will be released on April 2nd (physical version). The eBook version will follow soon after, on April 15th. You can preorder the pBook now on the Manning book page. The physical version comes with a complimentary eBook version (when the eBook is released), in all three formats: PDF, ePub, and Kindle.

If you haven’t yet, order it now!

(softbound 416 pages, black and white; includes access to color PDF, ePub and Kindle when available)

The release date for Zumel, Mount “Practical Data Science with R” is getting close. I thought I would share a few statistics about what goes into this kind of book. Read more…

There’s a new post up at the ninazumel.com blog that looks at the statistics of “verification by multiplicity” — the statistical technique that is behind NASA’s announcement of 715 new planets that have been validated in the data from the Kepler Space Telescope.

We normally don’t write about science here at Win-Vector, but we do sometimes examine the statistics and statistical methods behind scientific announcements and issues. NASA’s new technique is a cute and relatively straightforward (statistically speaking) approach.

From what I understand of the introduction to the paper, there are two ways to determine whether or not a planet candidate is really a planet: the first is to confirm the fact with additional measurements of the target star’s gravitational wobble, or by measurements of the transit times of the apparent planets across the face of the star. Getting sufficient measurements can take time. The other way is to “validate” the planet by showing that it’s highly unlikely that the sighting was a false positive. Specifically, the probability that the signal observed was caused by a planet should be at least 100 times larger than the probability that the signal is a false positive. The validation analysis is a Bayesian approach that considers various mechanisms that produce false positives, determines the probability that these various mechanisms could have produced the signal in question, and compares them to the probability that a planet produced the signal.

The basic idea behind verification by multiplicity is that planets are often clustered in multi-planet star systems, while false positive measurements (mistaken identification of potential planets) occur randomly. Putting this another way: if false positives are random, then they won’t tend to occur together near the same star. So if you observe a star with multiple “planet signals,” it’s unlikely that all the signals are false positives. We can use that observation to quantify how much more likely it is that a star with multiple candidates actually hosts a planet. The resulting probability can be used as an improved prior for the planet model when doing the statistical validation described above.

You can read the rest of the article here.

Please forward and share this discount offer for our upcoming book. Manning Deal of the Day February 22: Half off Practical Data Science with R. Use code dotd022214au at www.manning.com/zumel/.

The last appendix has gone to the editors; the book is now content complete. What a relief!

We are hoping to release the book late in the first quarter of next year. In the meantime, you can still get early drafts of our chapters through Manning’s Early Access program, if you haven’t yet. The link is here.

We look forward to sharing the final version of the book with you next year.

Please share: Manning Deal of the Day November 19: Half off Practical Data Science with R. Use code dotd1119au at www.manning.com/zumel/.