Archive for rand

JSM 2024, Portland, Day 1

Posted in pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , , , , , , , , , , on August 6, 2024 by xi'an

Strolling through the Oregon Conference Centre on 5 Aug, I am as always amazed at how the JSM conference centres have the ability to swallow in thousands of participants without giving an impression of overcrowding! (And appreciating the moderate air conditioning, which for once does not require wearing a fleece indoors!), I must admit that my first impressions of the city itself have been rather poor, as I was (unsuccessfully) seeking an after-hour grocery near the conference centre, I walked through run-down areas and kept passing homeless people, most in a sorry state. And hearing throughout the night, And again this morning in the warehouse maze I jogged through  before hitting the Willamette River path, which goes uninterrupted for miles. And showed me an unexpected spot, the Kevin Duckworth dock, where swimming the Willamette is feasible. Hopefully attempting a morn swim before I leave Portland.

I attended the quantum computing session, with a rather light introduction without bringing much light on the nature of qubits for storage and computing. In particular, the role of the complex coefficients of the 0 and 1 states. Then my friend Brani Vidakovic gave us an hand-on demonstration (almost hand-on as the conference facilities were unable to let a code run live!, eons away from quantum performances!!). Using Qiskit and Anaconda. He pointed out that measuring a qubit is destroying its quantum nature, the very equivalent of Schrôdinger’s cat. But does not make it clear whether or not the qubit later returns to a random entity, since frequency stabilisation is assumed, witness the histograms displayed by Brani. or a Nature paper of last year.

Went to a (poorly attended) privacy panel session next. With a defence of the value of differential privacy (Jordan Awan, Purdue) somewhat connected with our own (Ocean) work, although utility not understood in a decision-theoretic sense. And privacy remaining in an one-suits-all sense. With Michael Hawes from the US Census Bureau introducing more dimensions than mere DP (coarsening, suppression, swapping, &tc.), with legal aspects (Title 13) of disclosure risk. With the positive (for me) notion of providing a meaningful assessment of disclosure, with some records being more vulnerable than others. And another one on the cumulative disclosure risk over time. If short in quantitative entries. With Valbona Bejleri (USDA NASS, with a Census of their own) on cell suppressions and metrics for assessing disclosure, eg Shannon’s information entropy. And with Gary Howarth (Privacy Engineering Program, NIST). whose point remained rather unclear to me, like the apparently obvious point that adding features increase dispersion between populations. Although presenting tools for convincing experts and actors of the efficiency of privacy protection techniques.

Which continued (sort of) on the afternoon with a synthetic data for preserving privacy panel session. With Bradley Malin (Vanderbilt U) on the dangers of (Nature Communication paper of 2022). And Harrison Quick using a posterior predictive to achieve differential privacy, albeit considering the posterior predictive as the statistical analysis outcome does not seem the right focus (and recoup my earlier criticism of differential privacy requiring bending one’s prior beliefs). Indeed, as a Bayesian aiming at inference rather than merely at not releasing raw data, I would use synthetic data generated from that posterior predictive to return a posterior on the parameters of interest. And Joshua Snoke (RAND) with cautionary warnings. Like producing “invalid” inference because the reliance on a specific model (but isn’t that the case for most of statistics?). And Roee Gutman (Brown U), discussing the special case of record linkage. More into the difficulties in creating synthetic data. (Like the issue with missingness.) Somehow disappointing in not reaching a more statistical and quantitative perspective, eg by sticking to a Bayesian perspective the whole way.

On a non-technical side, I am surprised at hardly anyone adopting the bring-your-own-container policy in the OCC cafés, or outside, given the large number of attendees carrying one or several liquid containers.

my own personal hope for the future is that we won’t have to build any more random number generators…

Posted in Books, Statistics, University life with tags , , , , , , on April 19, 2020 by xi'an

Came perchance upon this reminiscence about the generation of the 10⁶ random digits found in the book published by the RAND Corporation. It took them a month to produce half a million digits, exploiting a “random frequency pulse source gated by a constant frequency pulse” behaving like a “roulette wheel with 32 positions, making on the average 3000 revolutions on each turn”. As the outcome failed on the odd/even ratio test, the RAND engineers randomized further the outcome by adding “(mod 10) the digits in each card, digit by digits, to the corresponding digits of the previous card”. (Cards as in punched cards, the outcome being printed 50 digits at a time on I.B.M. cards.) A last piece of Monte Carlo trivia is that the electronic roulette at the basis of this random generator was devised by Hastings, Cecil not Wilfred Keith. (And RAND is an abbreviation of Research and Development, not of randomness!)

Monte Carlo calculations of the radial distribution functions for a proton-electron plasma

Posted in Books, pictures, Statistics, University life with tags , , , , , , , , on October 11, 2017 by xi'an

“In conclusion, the Monte Carlo method of calculating radial distribution functions in a plasma is a feasible approach if significant computing time is available (…) The results indicate that at least 10000 iterations must be completed before the system can be considered near to its equilibrium state, and for a badly chosen starting configuration, the run would need to be considerably longer (…) for more conclusive results a longer run is needed so that the energy of the system can settle into an equilibrium pattern and steady-state radial distribution functions can be obtained.” A.A. Barker

Looking for the history behind Barker’s formula the other day made me look for the original 1965 paper. Which got published in the Australian Journal of Physics at the beginning of Barker’s PhD at the University of Adelaide.

As shown in the above screenshot, the basis  of Barker’s algorithm is indeed Barker’s acceptance probability, albeit written in a somewhat confusing way since the current value of the chain is kept if a Uniform variate is smaller than what is actually the rejection probability. No mistake there! And more interestingly, Barker refers to Wood and Parker (1957) for the “complete and rigorous theory” behind the method. (Both Wood and Parker being affiliated with Los Alamos Scientific Laboratory, while Barker acknowledges support from both the Australian Institute of Nuclear Science and Engineering and the Weapons Research Establishment, Salisbury… This were times when nuclear weapon research was driving MCMC. Hopefully we will not come back to such times. Or, on the pessimistic side, we will not have time to come back to such times!)

As in Metropolis et al. (1953), the analysis is made on a discretised (finite) space, building the Markov transition matrix, stating the detailed balance equation (called microscopic reversibility). Interestingly, while Barker acknowledges that there are other ways of assigning the transition probability, his is the “most rapid” in terms of mixing. And equally interestingly, he discusses the scale of the random walk in the [not-yet-called] Metropolis-within-Gibbs move as major, targetting 0.5 as the right acceptance rate, and suggesting to adapt this scale on the go. There is also a side issue that is apparently not processed with all due rigour, namely the fact that the particles in the system cannot get arbitrarily close from one another. It is unclear how a proposal falling below this distance is processed by Barker’s algorithm. When implemented on 32 particles, this algorithm took five hours to execute 6100 iterations. With a plot of the target energy function that does not shout convergence, far from it! As acknowledged by Barker himself (p.131).

The above quote is from the conclusion and its acceptance of the need for increased computing times comes as a sharp contrast with this week when one of our papers was rejected based on this very feature..!

atmospheric random generator?!

Posted in Books, Mountains, pictures, Statistics, Travel with tags , , , , , , , on April 10, 2012 by xi'an

As I was glancing through The Cleanest Line, (the outdoor clothing company) Patagonia‘s enjoyable—as long as one keeps in mind Patagonia is a company, although with commendable ethical and ecological goals—blog, I came upon this entry “And the Winner of “Chasing Waves” is …” where the name of the winner of the book Chasing Wave was revealed. (Not that I am particularly into surfing…!) The interesting point to which I am coming so circumlocutory (!) is that they use a random generator based on atmospheric noise to select the winner! I particularly like the sentence that the generator “for many purposes is better than the pseudo-random number algorithms typically used in computer programs”. For which purpose exactly?!

Now, to be (at least a wee) fair, the site of random.org contains an explanation about the quality of their generator. I am however surprised by the comparison they run with the rand() function from PHP on Microsoft Windows, since it produces a visible divergence from uniformity on a bitmap graph… Further investigation led to this explanation of the phenomenon, namely the inadequacy of the PHP language rather than of the underlying (pseudo-)random generator. (It had been a while since I had a go at this randomness controvery!)