John D. Cook

Objectives and constraints are symmetrical in a mathematical sense but are asymmetrical in a psychological sense. By taking dual formulations, you can reverse the mathematical role of objectives and constraints, but in application objectives are more obvious than constraints. In the question “What is the minimum value of x² over the interval [1, 5]?” the […]

In the previous post I said that the Jacobi functions are like trig functions. That’s true if you look along the real axis. If you look at the rest of the complex plane you’ll see how they can be very different. The sine function is periodic along the real axis, but it grows exponentially along […]

Jacobi’s elliptic functions are sorta like trig functions. His functions sn and cn have names that reminiscent of sine and cosine for good reason. These functions come up in applications such as the nonlinear pendulum (i.e. when θ is too large to assume θ is a good enough approximation to sin θ) and in conformal […]

The complex numbers make a field out of pairs of real numbers. The quaternions almost make a field out of four-tuples of numbers, though multiplication is not commutatative. Technically, quaternions form a division algebra. Frobenius’s theorem says only real vector spaces that can be made into division algebras are the real numbers, complex numbers, and […]

Here are three things about dominoes, two easy and one more advanced. Counting First, how many pieces are there in a set of dominoes? A domino corresponds to an unordered pair of numbers from 0 to n. The most popular form has n = 6, but there are variations with other values of n. You can show that […]

I asked two questions on twitter yesterday. The previous post summarized the results for a question about books that I asked from my personal Twitter account. This post will summarize the results of a question I asked from @AnalysisFact. If a genie offered to give you a thorough understanding of one theorem, what theorem would […]

I asked on Twitter today for books that people would like to have read, but don’t want to put in the time and effort to read. What’s a book you would like to have read but don’t want to read? — John D. Cook (@JohnDCook) June 15, 2018 Here are the responses I got, organized […]

Here are a couple of linear algebra identities that can be very useful, but aren’t that widely known, somewhere between common knowledge and arcane. Neither result assumes any matrix has low rank, but their most common application, at least in my experience, is in the context of something of low rank added to something of […]

Here are two apparently unrelated things you may have seen before. The first is an observation going back to Euler that the polynomial produces a long sequence of primes. Namely, the values are prime for n = 1, 2, 3, …, 40. The second is that the number is extraordinarily close to an integer. This number […]

There’s a proposal for California to split into three states. If that happens, what would happen to the US flag? The US flag has had 13 stripes from the beginning, representing the first 13 states. The number of stars has increased over time as the number of states has increased. Currently there are 50 stars, […]

The partition function p(n) counts the number of ways n unlabeled things can be partitioned into non-empty sets. (Contrast with Bell numbers that count partitions of labeled things.) There’s no simple expression for p(n), but Ramanujan discovered a fairly simple asymptotic approximation: How accurate is this approximation? Here’s a little Matheamtica code to see. p[n_] := PartitionsP[n] approx[n_] […]

I like Perl’s pattern matching features more than Perl as a programming language. I’d like to take advantage of the former without having to go any deeper than necessary into the latter. The book Minimal Perl is useful in this regard. It has chapters on Perl as a better grep, a better awk, a better […]

I just finished listening to the latest episode of Twenty Thousand Hertz, the story behind “Deep Note,” the THX logo sound. There are a couple mathematical details of the sound that I’d like to explore here: random number generation, and especially Pythagorean tuning. Random number generation First is that part of the construction of the […]

Stirling numbers are something like binomial coefficients. They come in two varieties, imaginatively called the first kind and second kind. Unfortunately it is the second kind that are simpler to describe and that come up more often in applications, so we’ll start there. Stirling numbers of the second kind The Stirling number of the second […]

Start with the sequence of positive integers: 1, 2, 3, 4, … Now take partial sums, the nth term of the new series being the sum of the first n terms of the previous series. This gives us the triangular numbers, so called because they count the number of coins at each level of a […]

The nth Bell number is the number of ways to partition a set of n labeled items. It’s also equal to the following sum. You may have to look at that sum twice to see it correctly. It looks a lot like the sum for en except the roles of k and n are reversed in […]

If you average a large number independent versions of the same random variable, the central limit theorem says the average will be approximately normal. That is the absolute error in approximating the density of the average by the density of a normal random variable will be small. (Terms and conditions apply. See notes here.) But […]

I was playing around with something this afternoon and stumbled on something like Gibbs phenomena or Runge phenomena for the Central Limit Theorem. The first place most people encounter Gibbs phenomena is in Fourier series for a step function. The Fourier series develops “bat ears” near the discontinuity. Here’s an example I blogged about before […]

Let me say up front that relying on the normal distribution as an accurate model of extreme events is foolish under most circumstances. The main reason to calculate the probability of, say, a 40 sigma event is to show how absurd it is to talk about 40 sigma events. See my previous post on six-sigma […]

I saw on Twitter this afternoon a paraphrase of a quote from Nassim Taleb to the effect that if you see a six-sigma event, that’s evidence that it wasn’t really a six-sigma event. What does that mean? Six sigma means six standard deviations away from the mean of a probability distribution, sigma (σ) being the […]

Calendars are based on three frequencies: the rotation of the Earth on its axis, the rotation of the moon around the Earth, and the rotation of the Earth around the sun. Calendars are complicated because none of these periods is a simple multiple of any other. The ratios are certainly not integers, but they’re not […]

Erik Erlandson sent me a note saying he found my post on computing the soft maximum helpful. (If you’re unfamiliar with the soft maximum, here’s a brief description of what it is and how you might use it.) Erik writes I used your post on practical techniques for computing smooth max, which will probably be […]

NASA’s Galileo mission was primarily designed to explore Jupiter and its moons. In 1989, the Galileo probe started out traveling away from Jupiter in order to do a gravity assist swing around Venus. About a year later it also did a gravity assist maneuver around Earth. Carl Sagan suggested that when passing Earth, the Galileo […]

Most basic combinatorial problems can be solved in terms of multiplication, permutations, and combinations. The next step beyond the basics, in my experience, is counting selections with replacement. Often when I run into a problem that is not quite transparent, it boils down to this. Examples of selection with replacement Here are three problems that […]

It’s easy to create rational approximations for π. Every time you write down π to a few decimal places, that’s a rational approximation. For example, 3.14 = 314/100. But that’s not the best approximation. Think of the denominator of your fraction as something you have to buy. If you have enough budget to buy a three-digit […]

Here’s an interesting problem that came out of a logistic regression application. The input variable was between 0 and 1, and someone asked when and where the logistic transformation f(x) = 1/(1 + exp(a + bx)) has a fixed point, i.e. f(x) = x. So given logistic regression parameters a and b, when does the logistic curve given by y […]

A new video from 3Blue1Brown is about visualizing derivatives as stretching and shrinking factors. Along the way they consider the function f(x) = 1 + 1/x. Iterations of f converge on the golden ratio, no matter where you start (with one exception). The video creates a graph where they connect values of x on one […]

I just made one of those O’Reilly parody book covers. It’s a joke on Judea Pearl, expert in causal inference, and the Perl programming language, known for its unusual, terse syntax. Related:

When I was a teenager, my uncle gave me a calculus book and told me that mastering calculus was the most important thing I could do for starting out in math. So I learned the basics of calculus from that book. Later I read Michael Spivak’s two calculus books. I took courses that built on […]

The two-sample t-test is a way to test whether two data sets come from distributions with the same mean. I wrote a few days ago about how the test performs under ideal circumstances, as well as less than ideal circumstances. This is an analogous post for testing whether two data sets come from distributions with the same […]

To first approximation, Earth is a sphere. A more accurate description is that the earth is an oblate spheroid, the polar axis being a little shorter than the equatorial diameter. See details here. Other planets are also oblate spheroids as well. Jupiter is further from spherical than the earth is more oblate. The general equation […]

A two-sample t-test is intended to determine whether there’s evidence that two samples have come from distributions with different means. The test assumes that both samples come from normal distributions. Robust to non-normality, not to asymmetry It is fairly well known that the t-test is robust to departures from a normal distribution, as long as the actual […]

The latest episode of My Favorite theorem features John Urschel, former offensive lineman for the Baltimore Ravens and current math graduate student. His favorite theorem is a result on graph approximation: for every weighted graph, no matter how densely connected, it is possible to find a sparse graph whose Laplacian approximates that of the original […]

Here’s an interesting little tidbit: For any prime p except 2 and 5, the decimal expansion of 1/p repeats with a period that divides p-1. The period could be as large as p-1, but no larger. If it’s less than p-1, then it’s a divisor of p-1. Here are a few examples. 1/3 = 0.33… […]

This morning I ran across the following graph via Horace Dediu. I developed Windows software during the fattest part of the Windows curve. That was a great time to be in the Windows ecosystem. Before that I was in an academic bubble. My world consisted primarily of Macs and various flavors of Unix. I had […]

P. J. Huber gives three desiderata for a statistical method in his book Robust Statistics: It should have a reasonably good (optimal or nearly optimal) efficiency at the assumed model. It should be robust in the sense that small deviations from the model assumptions should impair the performance only slightly. Somewhat larger deviations from the […]

Matrix compression Suppose you have an m by n matrix A, where m and n are very large, that you’d like to compress. That is, you’d like to come up with an approximation of A that takes less data to describe. For example, consider a high resolution photo that as a matrix of gray scale values. An approximation to the matrix […]

If often happens in applications that a linear system of equations Ax = b either does not have a solution or has infinitely many solutions. Applications often use least squares to create a problem that has a unique solution. Overdetermined systems Suppose the matrix A has dimensions m by n and the right hand side vector b has dimension m. Then the […]

In a nutshell, given the singular decomposition of a matrix A, the Moore-Penrose pseudoinverse is given by This post will explain what the terms above mean, and how to compute them in Python and in Matheamtica. Singular Value Decomposition (SVD) The singular value decomposition of a matrix is a sort of change of coordinates that makes […]

The previous post looked at how probability predictions from a logistic regression model vary as a function of the fitted parameters. This post goes through the same exercise for probit regression and compares the two kinds of nonlinear regression. Generalized linear models and link functions Logistic and probit regression are minor variations on a theme. […]

The output of a logistic regression model is a function that predicts the probability of an event as a function of the input parameter. This post will only look at a simple logistic regression model with one predictor, but similar analysis applies to multiple regression with several predictors. Here’s a plot of such a curve […]

Tridiagonal matrices A tridiagonal matrix is a matrix that has nonzero entries only on the main diagonal and on the adjacent off-diagonals. This special structure comes up frequently in applications. For example, the finite difference numerical solution to the heat equation leads to a tridiagonal system. Another application, the one we’ll look at in detail […]

The latest blog post from Gödel’s Lost Letter and P=NP looks at the problem of finding relatively prime pairs of large numbers. In particular, they want a deterministic algorithm. They mention in passing that the probability of a set of k large integers being relatively prime (coprime) is 1/ζ(k) where ζ is the Riemann zeta function. This […]

What could be interesting about the lowly quadratic formula? It’s a formula after all. You just stick numbers into it. Well, there’s an interesting wrinkle. When the linear coefficient b is large relative to the other coefficients, the quadratic formula can give wrong results when implemented in floating point arithmetic. Quadratic formula and loss of precision The […]

There was a discussion on Twitter today about a mistake calculus students make: I pointed out that it’s only off by one character: The first equation is simply wrong. The second is correct, but a gross violation of convention, using x as a constant and e as a variable.

I’ve started a new Twitter account: @BasicStatistics. The new account is for people who are curious about statistics. It’s meant to be accessible to a wider audience than @DataSciFact. More Twitter accounts here.

Bernstein’s Matrix Mathematics is impressive. It’s over 1500 pages and weighs 5.3 pounds (2.4 kg). It’s a reference book, not the kind of book you just sit down to read. (Actually, I have sat down to read parts of it.) I’d used a library copy of the first edition, and so when Princeton University Press […]

The Moore-Penrose pseudoinverse of a matrix is a way of coming up with something like an inverse for a matrix that doesn’t have an inverse. If a matrix does have an inverse, then the pseudoinverse is in fact the inverse. The Moore-Penrose pseudoinverse is also called a generalized inverse for this reason: it’s not just […]

One of my complaints about math writing is that definitions are hardly ever subtractive, even if that’s how people think of them. For example, a monoid is a group except without inverses. But that’s not how you’ll see it defined. Instead you’ll read that it’s a set with an associative binary operation and an identity […]

A probability distribution is called “fat tailed” if its probability density goes to zero slowly. Slowly relative to what? That is often implicit and left up to context, but generally speaking the exponential distribution is the dividing line. Probability densities that decay faster than the exponential distribution are called “thin” or “light,” and densities that […]