Nick Solomon
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Tue, 06 Feb 2018 00:00:00 +0000

Learn MetropolisHastings Sampling with R
/post/learnmetropolishastingssamplingwithr/
Tue, 06 Feb 2018 00:00:00 +0000
/post/learnmetropolishastingssamplingwithr/
In this blog post I hope to introduce you to the powerful and simple MetropolisHastings algorithm. This is a common algorithm for generating samples from a complicated distribution using Markov chain Monte Carlo, or MCMC.
By way of motivation, remember that Bayes’ theorem says that given a prior \(\pi(\theta)\) and a likelihood that depends on the data, \(f(\theta  x)\), we can calculate \[ \pi(\theta  x) = \frac{f(\theta  x) \pi(\theta)}{\int f(\theta  x) \pi(\theta) \; \mathrm{d}\theta}.

How is Oregon Motor Voter affecting different counties?
/post/20170301howisoregonmotorvoteraffectingdifferentcounties/
Wed, 01 Mar 2017 00:00:00 +0000
/post/20170301howisoregonmotorvoteraffectingdifferentcounties/
(This is a companion to my post on Paul Gronke’s earlyvoting.net)
One of the first assignments we had in my Election Sciences course was to take a look at registration data from the Oregon Motor Voter program and try to find interesting patterns. For those who don’t know, Oregon Motor Voter is an automatic voter registration program in Oregon. Whenever someone interacts with the Oregon DMV, their voter eligibility is automatically checked, and if they are eligible to vote but not registered, they are automatically added to the rolls.

Cases handled by Legal Aid of Southeastern PA
/pages/lasp/
Tue, 07 Feb 2017 21:51:44 0800
/pages/lasp/
For this project, I worked with Legal Aid of Southeastern PA to help them find patterns in the types of services they provide to different geographic areas in the four counties they serve. Here is an anonymized version of the interactive maps I produced for them. Points were jittered randomly by a small amount, so while exact locations are inaccurate, any overall trends should still be apparent.
Bucks county Chester county Delaware county Montgomery county

Networks and disease
/post/20170123networksanddisease/
Mon, 06 Feb 2017 00:00:00 +0000
/post/20170123networksanddisease/
In 1861 the small town of Hagelloch, Germany experienced a measles outbreak. A doctor very carefully recorded the time of infection and symptoms for each patient.1 In the 1990’s, another German doctor went through all this data and was able to deduce the source of infection of each child.2 This data set gives us a wealth of information about the spread of disease, but it also allows us the rare opportunity to view disease as spreading over a network.

About
/pages/about/
Tue, 17 Jan 2017 16:03:55 0800
/pages/about/
I’m working on course development with DataCamp
I like statistics, R, and Python.
I wrote a thesis about asymptotic properties of network models.
All the photos were taken by me with a Minolta X370 in various places in Oregon, Washington, and Maine.

Projects
/pages/projects/
Tue, 17 Jan 2017 16:03:51 0800
/pages/projects/
Local Dependence in Exponential Random Network Models For my undergraduate thesis project at Reed College, I worked with random graph models used in social network analysis. I extended results about the existence and consistency of MLEs and the asymptotic distribution of statistics to a new class of model.
Crime in Northwest Philadelphia An exploration of the locations of violent crime in northwest Philadelphia from 2006 to 2016 using shiny and leaflet.