## La Défense, soleil couchant [#3]

Posted in pictures, Travel, University life with tags , , on December 6, 2013 by xi'an

## La Défense, soleil couchant [#2]

Posted in pictures, Travel, University life with tags , , , on December 5, 2013 by xi'an

## convergence speeds

Posted in pictures, Running, Statistics, Travel, University life with tags , , , , , , on December 5, 2013 by xi'an

While waiting for Jean-Michel to leave a thesis defence committee he was part of, I read this recently arXived survey by Novak and Rudolf, Computation of expectations by Markov chain Monte Carlo methods. The first part hinted at a sort of Bernoulli factory problem: when computing the expectation of f against the uniform distribution on G,

For x ∈ G we can compute f (x) and G is given by a membership oracle, i.e. we are able to check whether any x is in G or not.

However, the remainder of the paper does not get (in) that direction but recalls instead convergence results for MCMC schemes under various norms. Like spectral gap and Cheeger’s inequalities. So useful for a quick reminder, e.g. to my Monte Carlo Statistical Methods class Master students, but altogether well-known. The paper contains some precise bounds on the mean square error of the Monte Carlo approximation to the integral. For instance, for the hit-and-run algorithm, the uniform bound (for functions f bounded by 1) is

$9.5\cdot 10^{7}\dfrac{dr}{\sqrt{n}}+6.4\cdot 10^{15}\dfrac{d^2r^2}{n}$

where d is the dimension of the space and r a scale of the volume of G. For the Metropolis-Hastings algorithm, with (independent) uniform proposal on G, the bound becomes

$\dfrac{2C\alpha_dr^d}{n}+\dfrac{4C^2\alpha_d^2r^{2d}}{n^2}\,,$

where C is an upper bound on the target density (no longer the uniform). [I rephrased Theorem 2 by replacing vol(G) with the containing hyper-ball to connect both results, αd being the proportionality constant.] The paper also covers the case of the random walk Metropolis-Hastings algorithm, with the deceptively simple bound

$1089\dfrac{(d+1)\max\{\alpha,\sqrt{d+1}\}}{\sqrt{n}}+8.38\cdot 10^5\dfrac{(d+1)\max\{\alpha^2,d+1\}}{n}$

but this is in the special case when G is the ball of radius d. The paper concludes with a list of open problems.

## La Défense, soleil couchant [#1]

Posted in pictures, Travel, University life with tags , , , on December 4, 2013 by xi'an

## “an outstanding paper that covers the Jeffreys-Lindley paradox”…

Posted in Statistics, University life with tags , , , , , , , , on December 4, 2013 by xi'an

“This is, in this revised version, an outstanding paper that covers the Jeffreys-Lindley paradox (JLP) in exceptional depth and that unravels the philosophical differences between different schools of inference with the help of the JLP. From the analysis of this paradox, the author convincingly elaborates the principles of Bayesian and severity-based inferences, and engages in a thorough review of the latter’s account of the JLP in Spanos (2013).” Anonymous

I have now received a second round of reviews of my paper, “On the Jeffreys-Lindleys paradox” (submitted to Philosophy of Science) and the reports are quite positive (or even extremely positive as in the above quote!). The requests for changes are directed to clarify points, improve the background coverage, and simplify my heavy style (e.g., cutting Proustian sentences). These requests were easily addressed (hopefully to the satisfaction of the reviewers) and, thanks to the week in Warwick, I have already sent the paper back to the journal, with high hopes for acceptance. The new version has also been arXived. I must add that some parts of the reviews sounded much better than my original prose and I was almost tempted to include them in the final version. Take for instance

“As a result, the reader obtains not only a better insight into what is at stake in the JLP, going beyond the results of Spanos (2013) and Sprenger (2013), but also a much better understanding of the epistemic function and mechanics of statistical tests. This is a major achievement given the philosophical controversies that have haunted the topic for decades. Recent insights from Bayesian statistics are integrated into the article and make sure that it is mathematically up to date, but the technical and foundational aspects of the paper are well-balanced.” Anonymous