The collected works of Paul Erdõs are now available online. That’s 2% of the twentieth-century mathematical literature right there.
Via God Plays Dice.
The collected works of Paul Erdõs are now available online. That’s 2% of the twentieth-century mathematical literature right there.
Via God Plays Dice.
As I’m sure everyone has heard by now, the site Career Cast announced the top professions. Number 1 is mathematician, number 2 is actuary, and number 3 is statistician. The top of the list features other taxing careers such as accountant and historian. It sounds like the metric was the reciprocal of physical exertion.
Aimless websurfing has taught me that mathematics is apodictic. Now you know.
I was musing on the fact that I have never heard a psychologically plausible account of the appeal of pure mathematics. (I say “pure” mathematics because I suspect pure and applied mathematics have different sources of appeal). By “psychologically plausible”, I mean one grounded on the psychology of individual mathematicians. Lots of mathematicians have written explanations of the appeal, but most of these are either of the form “Because mathematics is awesome.”, or “Because I’m awesome” While mathematics is awesome, and while I’m willing to grant the premise that I’m awesome pretty much any time it comes up, these explanations lack the kind of specificity I have in mind. One common explanation, for example, is that math is like music, which relies on the presupposition that music is intrinsically valuable, and that math has value by analogy. But why do we like music? What in the psychology of mathematicians makes math seem like music to them? These are harder questions than the original one. Another explanation is that math is challenging, which is a subspecies of the “I’m awesome”. But in what way is mathematics challenging to mathematicians? Mathematicians, as a group, do not strive to be Nietzschean superman endlessly trying to overcome their limitations, so why this particular challenge, rather than the Nathan’s hot dog eating contest, or climbing Everest?
There are psychological explanations floating around as stereotypes, most of which are immensely unflattering, but are least examples of the kind of explanation I have in mind. One example is that mathematicians are like the Rain Man in that they just like repetitive tasks like counting or adding. Another example is that mathematicians can’t handle the real world, and so retreat to the safety of the world of numbers. These are both wrong and insulting, but they are at least grounded in the psychology of individual mathematicians. If anyone has a non-wrong explanation, I’d be curious to hear it.
Cosmic Variance has a cute little tradition where for each Thanksgiving Day they pick a physics result to be thankful for. This year they pick the spin-statistics theorem, which explains why elementary particles with half-integer spins satisfy the Pauli exclusion principle.
Slashdot has a thread discussing physics books recommendations for a math Ph.D. student who would like more physical intution into partial differential equations. There are some good suggestions, and many comments that come dangerously close to “You’re a math Ph.D. student and you don’t know what a PDE is?”
New Scientist has an interesting article, Where do science supermachines go when they die? that talks about what happens to the pieces of particle accelerators after they are decommissioned (or in the case of the Superconducting Supercollider, never turned on).
In the comments at n-category cafe, Zoran Skoda presents evidence that the journal Chaos, Solitons, and Fractals, a peer-reviewed journal published by Elsevier, is publishing pseudo-science. John Baez collects more evidence here. The journal is included in some of Elsevier’s journal bundles, so if you are at a school with a big library, you probably have access to the journal in electronic form, and can check it out yourself.
From the request thread, I was hoping for a nice easy softball, maybe from an undergraduate or mathematical amateur. Apparently, though, I have finally scared off anyone other than procrastinating professional mathematicians, who want me to actually write the posts I promised.
In the comments here I promised a post explaining why most statistics satisfy the Central Limit Theorem. I thought I’d start slowly with an explanation of what a statistic is.
A statistic is just something you compute from the data. This definition is so uninteresting that statistics books are a little apologetic about how contentless the definition sounds. (This usage of the term “statistic” was coined by Fisher. There is a cutting quote by Pearson on the terminology that is impossible to Google for, since all I remember is that it’s about the word statistic, and it involves Fisher and Pearson, who are probably the two most famous statisticians.)
Probability distributions are mathematical abstractions, while statistics are numbers we compute from actual data. If we believe that we can model that data as if it is generated by a random variable, then we have to relate the statistic to some property of the probability distribution. Usually, we are interested in some property of the underlying distribution, and using statistics to estimate it. For example, we may be interested in the mean of the underlying random variable, which we can approximate by using the mean of data.
Approximating the mean of the random variable in this way is a special case of a general technique to compute a property of a random variable. A random sample drawn from a probability distribution can be thought of as a (discrete) probability distribution in its own right. The property for the sample distribution can be used as an estimate of the property for the true distribution — this is known as the plug-in estimate for the property. An analog of the law of large numbers shows that this estimate converges to the true value.
Next time: the analogue of the central limit theorem.