Blog Archives

Bayesian methods at Bletchley Park

July 25, 2017
By
Bayesian methods at Bletchley Park

From Nick Patterson’s interview on Talking Machines: GCHQ in the ’70s, we thought of ourselves as completely Bayesian statisticians. All our data analysis was completely Bayesian, and that was a direct inheritance from Alan Turing. I’m not sure this has ever really been published, but Turing, almost as a sideline during his cryptoanalytic work, reinvented […]

Read more »

Testing the PCG random number generator

July 7, 2017
By

M. E. O’Neill’s PCG family of random number generators looks very promising. It appears to have excellent statistical and cryptographic properties. And it takes remarkably little code to implement. (PCG stands for Permuted Congruential Generator.) The journal article announcing PCG gives the results of testing it with the TestU01 test suite. I wanted to try it out […]

Read more »

Effective sample size for MCMC

June 27, 2017
By
Effective sample size for MCMC

In applications we’d like to draw independent random samples from complicated probability distributions, often the posterior distribution on parameters in a Bayesian analysis. Most of the time this is impractical. MCMC (Markov Chain Monte Carlo) gives us a way around this impasse. It lets us draw samples from practically any probability distribution. But there’s a […]

Read more »

Why do linear prediction confidence regions flare out?

June 26, 2017
By

Suppose you’re tracking some object based on its initial position x0 and initial velocity v0. The initial position and initial velocity are estimated from normal distributions with standard deviations σx and σv. (To keep things simple, let’s assume our object is moving in only one dimension and that the distributions around initial position and velocity […]

Read more »

Extreme beta distributions

June 20, 2017
By
Extreme beta distributions

A beta probability distribution has two parameters, a and b. You can think of these as the number of successes and failures out of a+b trials. The PDF of a beta distribution is approximately normal if a and b are approximately equal and a + b is large. If a and b are close, they don’t have to be very large for the beta […]

Read more »

Quantile-quantile plots and powers of 3/2

April 2, 2017
By
Quantile-quantile plots and powers of 3/2

This post serves two purposes. It will empirically explore a question in number theory and demonstrate quantile-quantile (q-q) plots. It will shed light on a question raised in the previous post. And if you’re not familiar with q-q plots, it will serve as an introduction to such plots. The previous post said that for almost all x > […]

Read more »

Freudian hypothesis testing

March 23, 2017
By
Freudian hypothesis testing

In his paper Mindless statistics, Gerd Gigerenzer uses a Freudian analogy to describe the mental conflict researchers experience over statistical hypothesis testing. He says that the “statistical ritual” of NHST (null hypothesis significance testing) “is a form of conflict resolution, like compulsive hand washing.” In Gigerenzer’s analogy, the id represents Bayesian analysis. Deep down, a […]

Read more »

Big data and the law

February 2, 2017
By
Big data and the law

Excerpt from the new book Big Data of Complex Networks: Big Data and data protection law provide for a number of mutual conflicts: from the perspective of Big Data analytics, a strict application of data protection law as we know it today would set an immediate end to most Big Data applications. From the perspective of […]

Read more »

Subjectivity in statistics

December 15, 2016
By
Subjectivity in statistics

Andrew Gelman on subjectivity in statistics: Bayesian methods are often characterized as “subjective” because the user must choose a prior distribution, that is, a mathematical expression of prior information. The prior distribution requires information and user input, that’s for sure, but I don’t see this as being any more “subjective” than other aspects of a […]

Read more »

Interim analysis, futility monitoring, and predictive probability

October 19, 2016
By
Interim analysis, futility monitoring, and predictive probability

An interim analysis of a clinical trial is an unusual analysis. At the end of the trial you want to estimate how well some treatment X works. For example, you want to how likely is it that treatment X works better than the control treatment Y. But in the middle of the trial you want to know something more subtle. It’s […]

Read more »


Subscribe

Email:

  Subscribe