Category Archives: TCS

New book on Probabilistic Programming

This crossed my inbox recently:

The e-book is available now from https://www.manning.com/books/practical-probabilistic-programming. The print version will be available on Friday.

>From the cover:

Practical Probabilistic Programming introduces the working programmer to probabilistic programming. In it, you’ll learn how to use the PP paradigm to model application domains and then express those probabilistic models in code. Although PP can seem abstract, in this book you’ll immediately work on practical examples, like using the Figaro language to build a spam filter and applying Bayesian and Markov networks, to diagnose computer system data problems and recover digital images.

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Reddit asks about IBM Watson

Is IBM Watson just (mostly) marketing? (self.MachineLearning)

https://www.reddit.com/r/MachineLearning/comments/3qmfbz/is_ibm_watson_just_mostly_marketing/

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Combinatorial Optimization for GIS

A real, applied problem in spatial epidemiology crossed my desk last week, and it turns out that it is a super-fun combinatorial optimization challenge, too.

Details here: http://gis.stackexchange.com/questions/126280/group-polygon-features-to-match-a-set-of-specifications

I don’t have time to play around with it a lot now, but I did try a little stochastic search, which makes me think that this will not be trivial to solve:

bad_clustering

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How do you argue a query is impossible in SPARQL?

I’ve come to believe that I’m stumped in querying the existence of (s,t)-paths with SPARQL 1.0 or the k nearest neighbors with SPARQL 1.1 because of limitations of the query language, not limitations in my query-writing ability. But how to prove it? Or even provide some evidence? Tell me on cstheory.stackexachange.

Maybe this paper is relevant: Semantics and Complexity of SPARQL

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Computation time and model development

20 seconds, 20 minutes, or 20 hours.  These are all amounts of time that a computational method I’ve been working at some time has taken to complete processing.  They each lead to a very different experience for the model developer, and probably in the end for the model, too. Twenty seconds is definitely what I prefer.

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Age-heaping and Hedgehogs

I heard an interesting talk a few weeks ago about “age-heaping” in survey responses, the phenomenon where people remember ages imprecisely and say that their siblings are ages that are divisible by 5 much more often than expected.  There are some nice theory challenges here, with a big dose of stats modeling, but I’ll have to share some more thoughts on that later.

In the talk, the age-heaping was also referred to a a hedgehog or porcupine plot, because of the spikey histogram that the data produces.  I was looking for a nice picture of one, or some additional background reading, and when I searched for “hedgehog statistical plots”, all google would give me was a bunch of pages about stats on actual hedgehogs.  Cute!

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Algorithms in the Field Workshop

Last week I attended the workshop Algorithms in the Field or “8F”, as its puzzling acronym turns out to be. David Eppstein published comprehensive notes on the talks on his blog:

I like to think that I’m doing “8F” in my global health job, and also some algorithms in the forest, algorithms in the desert, etc. But this workshop gave me a chance to think about the connection to Algorithms with a capitol “A”, the kind going on in the theory group of the ivory towered computer science department. I’ve been pretty successful at coming up with little challenges in global health where algorithmic thinking is useful (e.g. Doctors = Noise Machines), and I’m going to try to use the blog to throw more of these puzzles over the fence in the next few weeks. My barrier to doing this in the past has been the amount of background research I need to do to avoid sounding foolish. But my writing style was never suited to caution. Let’s see how it goes. I hope that even half-explained connections between Algorithms and global health will inspire some algorithmitician to fill in the details.

What I challenge myself to do more of is to go beyond the little puzzles, and synthesize something bigger to ask from algorithmists. What algorithmic innovation would really change how we’re doing things in Global Health? This is a domain where avoiding foolishness is even more of a recipe for silence. But I will try.

On my mind now: the computation time necessary to fit a model. 20 seconds, 20 minutes, or 20 hours is a really big difference. More on that thought to come.

Question to readers from Global Health Departments, what do you think capitol-A algorithms researchers can offer us?

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