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/.
A quick status update on our upcoming book “Practical Data Science with R” by Nina Zumel and John Mount.
We are really happy with how the book is coming out. We were able to cover most everything we hoped to. Part 1 (especially chapter 3) is already being used in courses, and has some very good stuff on how to review data. Part 2 covers the “statistical / machine-learning canon,” and turns out to be a very complete demonstration of what odd steps are needed to move from start to finish for each example in R. Part 3 is going to finish with the important (but neglected) topics of delivering results to production, and building good documentation and presentations. Read more…
I’ll be talking at the “Official” BARUG meeting Wednesday, August 21, 2013. The RSVPs look full (sorry) but I wanted to post a thanks to the organizers for considering me. If things go well I’ll see if I can post the slides later (not sure if that is useful without detailed speakers notes). Read more…
Deal of the Day August 1: Half off my book Practical Data Science with R. Use code dotd0801au at www.manning.com/zumel/
A bit about our upcoming book “Practical Data Science with R”. Nina and I share our current draft of the front matter from the book, which is a description which will help you decide if this is the book for you (we hope that it is). Or this could be the book that helps explain what you do to others.