Blog Articles 166–170

Minneapolis is alt-transit awesomesauce

On my commute in to school today, there was a green-vested City of Minneapolis employee handing out survey cards to bicyclists on 15th Ave. SE.

Bicycling (and other alternative transit), while amazing and lots of fun, isn’t all roses. Last April, a bicyclist was killed by a semi truck at 15th Ave. SE and 4th St., an intersection I ride through every day.

But shortly thereafter (in response?), the city did some major work on 15th Ave. through Dinkytown, adding additional signage, painting the bike lanes bright green when they go through intersections, and other paintwork to increase bicycle path visibility (including a nice big box for bicyclists waiting to cross University). With the exception of the bike lane-bus interaction as southbound buses approach the on-campus bus stop, I think that these improvements have greatly improved bicycle visibility and navigability of this street.

And today’s survey? Now that the new signage and paint has been in place for a year, they’re surveying bicyclists to see whether they have noticed the new features, and whether they feel safe on the street.

Walkability as Freedom

Lee argues well that, while drivers enjoy freedom in a variety of environments, walkable urban settings (often with good transit) provide freedom for those unable to drive to live full and independent lives.

We’re somewhat in this situation ourselves - Jennifer cannot drive at night, but in a walkable environment, she can still go places in the evening (which, in Minnesota, is much of the day during some parts of the year).

I’ll just add that, though I can drive, I generally feel more free on foot, bike, or bus/train than in a car. I enjoy the wind on my face, the feeling of truly being in the city, the ability to take shortcuts and paths inaccessible to cars. I also have the freedom to enjoy my journey, destination, and company without worrying about where to park, or if I paid the meter enough. When I am finished, there will be another bus to take me home. I can live with freedom from worrying about if I need to fix the car, or how much registration and insurance will be.

Basically, I’m free to live rather than maintain, care for, and worry about the state and location of a silly metal box.

Guilty, of failing to prove yourself innocent of a crime

Justices Scalia and Thomas would have supported the Arizona law making it a crime for non-citizens to be without legal immigration papers. Immigration law is complex and filled with opportunities for unpredictable judge discretion. Many undocumented immigrants, once they get before a judge, discover they have a legal basis for being in the U.S. In our innocent-until-proven-guilty legal system, such immigrants have committed no crimes. The Arizona law not only assumed guilty-until-proven-innocent, it actually created the crime of not having previously proven oneself innocent of another crime. Thankfully, such state laws are now clearly unconstitutional.

Rundown of losing legal theories in Arizona v. United States. I found this one particularly poignant — I knew the AZ law went in the highly undesirable direction of a “Papers, please” state, but had not yet thought of it in quite this light.

Rock of Les Misérables

It’s struck me this week how a good number of songs by great rockers are deeply sympathetic to the plight of the down-and-out, the beaten up, the oppressed, those suffering bad luck or systemic injustice. In particular, a number of songs have been on my mind lately that tell of the recently, currently, or hopefully not incarcerated.

So from this week’s heavy rotation (and elsewhere in my collection), a few entries from the rock of les misérables that I’ve been pondering:

  • The Band — “The Shape I’m In”
  • Tom Petty and the Heartbreakers — “Don’t Pull Me Over”
  • Johnny Cash — “Folsom Prison Blues”, of course, but also “Give My Love to Rose” and one of my favorites, “Man in Black”

Some Motivations for Bayesian Statistics

If you’ve been following my Twitter stream, you have probably seen that I’m doing some reading and study on Bayesian statistics lately. For a variety of reasons, I find the Bayesian model of statistics quite compelling and am hoping to be able to use it in some of my research.

Traditional statistics, encapsulating well-known methods such as t-tests, ANOVA, etc. are from the frequentist school of statistical thought. The basic idea of frequentist statistics is that the world is described by parameters that are fixed and unknown. These parameters can be all manner of things — the rotation rate of the earth, the average life span of a naked mole rat, or the average number of kittens in a litter of cats. It is rare that we can have access to the entire population of interest (e.g. all mature female cats) to be able to directly measure the parameter, so we estimate parameters by taking random samples from the population, computing some statistic over the sample, and using that as our estimate of the population parameter. Since these parameters are unknown, we do not know their exact values. Since they are fixed, however, we cannot discuss them in probabilistic terms. Probabilistic reasoning only applies to random variables, and parameters are not random — we just don’t know what their values are. Probabilities, expected values, etc. are only meaningful in the context of the outcome of multiple repeated random experiments drawn from the population.

The Bayesian says, “Who cares?”. Bayesian statistics applies probabilistic methods and reasoning directly to the parameters. This doesn’t necessarily mean that the Bayesian thinks the world is really random, though. It turns out that we can use probabilities not only to express the chance that something will occur, but we can also use them to express the extent to which we believe something and the math all still works. So we can use the algebra of probabilities to quantify and describe how much we believe various propositions, such as “the average number of kittens per litter is 47”.

One of the fundamental differences, therefore, is that the frequentist can only apply probabilities to the act of repeating an experiment. The Bayesian can apply probabilities directly to their knowledge of the world. There are other important differences as well — frequentistic statistics is primarily concerned with testing and falsifying hypotheses, while Bayesian focuses more on determining which of several competing models or hypotheses is most likely to be true — but those differences play less of a role in what I find compelling about Bayesian statistics, and it is also possible to apply falsificationist principles in a Bayesian framework.