Truth About the Cause of [Clinton’s] Concussion
http://www.redstate.com/diary/checkmate2012/2015/04/11/hillary-lie-cause-concussion/ Wild post on a #conspiracy theory that’ll make a great movie plot
compare
A Deadly Deployment, a Navy SEAL’s Despair – The New York Times http://www.nytimes.com/2016/01/20/world/asia/navy-seal-team-4-suicide.html
Interesting mention of bioinformatics
http://www.wired.com/wired/archive/12.06/dating.html
Two foci for Genomics England are cancer and rare, inherited diseases – not neuro, cardio, &c.
https://twitter.com/timjph/status/353047985808084993
http://www.ft.com/intl/cms/s/0/6a1f001e-e48a-11e2-a74d-00144feabdc0.html#axzz2Y840CSnr
Biographer and editor James Atlas tells stories of real lives by Craig Lambert
September-October 2000
http://harvardmagazine.com/2000/09/a-short-proust-and-a-lon-html
class of ’88 at Yale & onto millions on Wall St. before an
investigation and now a 2nd act
http://www.newyorker.com/reporting/2013/04/08/130408fa_fact_auletta
Some thoughts on a good paper giving intuition on machine learning approaches
http://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf
http://dl.acm.org/citation.cfm?id=2347755
In particular, the paper gives good intuition about:
– overfitting (e.g. how it’s related to multiple testing & bias v variance)
– the curse of dimensionality (in high-D all neighbors look the same)
– the non-practicality of theoretical guarantees
– how different frontiers can give the same prediction
– ensembles (which reduce variance greatly without increasing bias that much)
– ensembles vs Bayesian model averaging (which essentially select the best model)
homes.cs.washington.edu/~pedrod/papers/cacm12.pdf
http://dl.acm.org/citation.cfm?id=2347755