Travel

This week I spent two days in Los Angeles visiting a client and two days in Austin for the SciPy conference.

The project in LA is very interesting, but not something I’m free to talk about. The conference was fun, especially because I got to see in person several people that I have only talked to online. Both trips stirred up a lot ideas that are going to take a while to process.

I may be going to Seattle before too long, but other than that I’m done traveling for now.

Clever visual proof

There are as many yellow dots above the bottom row of the triangle as there are pairs of purple dots on the bottom row. To see this, note that every yellow dot determines a pair of purple dots by projecting it down to the left and to the right. Conversely, you can go up from any pair of purple dots up to a yellow dot.

Via sark

Related post: Tetrahedral numbers

Statistical evidence versus legal evidence

This is the job of a juror in the US legal system described in statistical terms:

Compute the posterior probability of a defendant’s guilt conditioned on the admissible evidence, starting with a prior biased toward innocence. Report “guilty” if the posterior mean probability of guilt is above a level referred to as “beyond reasonable doubt.”

A juror is not to compute a probability conditioned on all evidence, only admissible evidence. One of the purposes of voir dire is to identify potential jurors who do not understand this concept and strike them from the jury pool. Very few jurors would understand or use the language of conditional probability, but a competent juror must understand that some facts are not to be taken into consideration in reaching a verdict.

For example, the fact that someone has been arrested, indicted by a grand jury, and brought to trial is not itself to be considered evidence of guilt. It is not legal evidence, but it certainly is statistical evidence: People on trial are more likely to be guilty of a crime than people who are not on trial.

This sort of schizophrenia is entirely proper. Statistical tendencies apply to populations, but trials are about individuals. The goal of a trial is to make a correct decision in an individual case, not to make correct decisions on average.[1]  Also, the American legal system embodies the belief that false positives are much worse than false negatives. [2]

Thinking of a verdict as a conditional probability allows a juror to simultaneously believe personally that someone is probably guilty while remaining undecided for legal purposes.

Related: A statistical problem with “nothing to hide”

Footnotes:

[1] Jury instructions are implicitly Bayesian rather than frequentist in the sense that jurors are asked to come up with a degree of belief. They are not asked to imagine an infinite sequence of similar trials etc.

[2] For example, Benjamin Franklin said  “That it is better 100 guilty Persons should escape than that one innocent Person should suffer, is a Maxim that has been long and generally approved.” In decision theory vocabulary, this is a highly asymmetric loss function.

How to convert frequency to pitch

I saw somewhere that James Earl Jones’ speaking voice is around 85 Hz. What musical pitch is that?

Let P be the frequency of some pitch you’re interested in and let C = 261.626 be the frequency of middle C. If h is the number of half steps from C to P then

P / C = 2h/12.

Taking logs,

h = 12 log(P / C) / log 2.

If P = 85, then h = -19.46. That is, James Earl Jones’ voice is about 19 half-steps below middle C, around the F an octave and a half below middle C.

More details on the derivation above here.

 

Photo credit Wikipedia
Music image created using Lilypod.

Leading digits and quadmath

My previous post looked at a problem that requires repeatedly finding the first digit of kn where k is a single digit but n may be on the order of millions or billions.

The most direct approach would be to first compute kn as a very large integer, then find it’s first digit. That approach is slow, and gets slower as n increases. A faster way is to look at the fractional part of log kn = n log k and see which digit it corresponds to.

If n is not terribly big, this can be done in ordinary precision. But when n is large, multiplying log k by n and taking the fractional part brings less significant digits into significance. So for very large n, you need extra precision. I first did this in Python using SymPy, then switched to C++ for more speed. There I used the quadmath library for gcc. (If n is big enough, even quadruple precision isn’t enough. An advantage to SymPy over quadmath is that the former has arbitrary precision. You could, for example, set the precision to be 10 more than the number of decimal places in n in order to retain 10 significant figures in the fractional part of n log k.)

The quadmath.h header file needs to be wrapped in an extern C declaration. Otherwise gcc will give you misleading error messages.

The 128-bit floating point type __float128 has twice as many bits as a double. The quadmath functions have the same name as their standard math.h counterparts, but with a q added on the end, such as log10q and fmodq below.

Here’s code for computing the leading digit of kn that illustrates using quadmath.

#include <cmath>
extern "C" {
#include <quadmath.h>
}

__float128 logs[11];

for (int i = 2; i <= 10; i++)
    logs[i] = log10q(i + 0.0);

int first_digit(int base, long long exponent)
{
    __float128 t = fmodq(exponent*logs[base], 1.0);
    for (int i = 2; i <= 10; i++)
        if (t < logs[i])
            return i-1;
}

The code always returns because t is less than 1.

Caching the values of log10q saves repeated calls to a relatively expensive function. So does using the search at the bottom rather than computing powq(10, t).

The linear search at the end is more efficient than it may seem. First, it’s only search a list of length 9. Second, because of Benford’s law, the leading digits are searched in order of decreasing frequency, i.e. most inputs will cause first_digit to return early in the search.

When you compile code using quadmath, be sure to add -lquadmath to the compile command.

Related posts

Benford’s law and SciPy
Leading digits of factorials

What have you been doing?

Several people have asked me what kind of work I’ve been doing since I went out on my own earlier this year. So far I’ve done a lot of fairly small projects, though I have one large project that’s just getting started. (The larger the project and client, the longer it takes to get rolling.)

Here are some particular things I’ve been doing.

  • Helped a company improve their statistical software development process
  • Modeled the efficiency and reliability of server configurations
  • Analyzed marketing and sales data
  • Coached someone in technical professional development
  • Wrote an article for an online magazine
  • Helped a company integrate R into their software product
  • Reviewed the mathematical code in a video game
  • Researched and coded up some numerical algorithms

If something on this list sounds like something you’d like for me to do with your company, please let me know.

 

Continuous quantum

David Tong argues that quantum mechanics is ultimately continuous, not discrete.

In other words, integers are not inputs of the theory, as Bohr thought. They are outputs. The integers are an example of what physicists call an emergent quantity. In this view, the term “quantum mechanics” is a misnomer. Deep down, the theory is not quantum. In systems such as the hydrogen atom, the processes described by the theory mold discreteness from underlying continuity. … The building blocks of our theories are not particles but fields: continuous, fluidlike objects spread throughout space. … The objects we call fundamental particles are not fundamental. Instead they are ripples of continuous fields.

Source: The Unquantum Quantum, Scientific American, December 2012.

Mean residual time

If something has survived this far, how much longer is it expected to survive? That’s the question answered by mean residual time.

For a positive random variable X, the mean residual time for X is a function eX(t) given by

e_X(t) = E(X - t \mid X > t) = \int_t^\infty  \frac{1 - F_X(x)}{1-F_X(t)} \, dx

provided the expectation and integral converge. Here F(t) is the CDF, the probability that X is less than t.

For an exponential distribution, the mean residual time is constant. For a Pareto (power law) distribution, the mean residual time is proportional to t. This has an interesting consequence, known as the Lindy effect.

Now let’s turn things around. Given function a function e(t), can we find a density function for a positive random variable with that mean residual time? Yes.

The equation above yields a differential equation for F, the CDF of the distribution.

If we differentiate both sides of

e(t) (1 - F(t)) = \int_t^\infty 1 - F(x)\, dx

with respect to t and rearrange, we get the first order differential equation

F'(t) + g(t)\, F(t) = g(t)

where

g(t) = \frac{e'(t) + 1}{e(t)}

The initial condition must be F(0) = 0 because we’re looking for the distribution of a positive random variable, i.e. the probability of X being less than zero must be 0. The solution is then

F(t) = 1 - \frac{e(0)}{e(t)} \exp\left( -\int_0^t \frac{dx}{e(x)} \right)

This means that for a desired mean residual time, you can use the equation above to create a CDF function to match. The derivative of the CDF function gives the PDF function, so differentiate both sides to get the density.

No use for old things

From Brave New World:

“But why is [Shakespeare] prohibited?” asked the Savage. …

The Controller shrugged his shoulders. “Because it’s old; that’s the chief reason. We haven’t any use for old things here.”

“Even when they’re beautiful?”

“Particularly when they’re beautiful. Beauty’s attractive, and we don’t want people to be attracted by old things. We want them to like the new ones.”

Related: Chronological snobbery

Pure math and physics

From Paul Dirac, 1938:

Pure mathematics and physics are becoming ever more closely connected, though their methods remain different. One may describe the situation by saying that the mathematician plays a game in which he himself invents the rules while the physicist plays a game in which the rules are provided by Nature, but as time goes on it becomes increasingly evident that the rules which the mathematician finds interesting are the same as those which Nature has chosen.

Singular Value Consulting, LLC

The name of my business is Singular Value Consulting, LLC.

Math people may catch the allusion to singular value decomposition (SVD). I hope that non-math folks will interpret “singular value” to mean something like “singularly valuable.”

One way to think of an SVD is a pair of coordinate systems that give a linear transformation the simplest representation. So metaphorically, SVD is getting to the core of a problem and producing a simple solution.

For some less serious mathematical company names, see this list.