pseudorandomness on Dan MacKinlayhttps://danmackinlay.name/tags/pseudorandomness.htmlRecent content in pseudorandomness on Dan MacKinlayHugo -- gohugo.ioen-usSun, 26 Sep 2021 09:11:03 +1000Algorithmic statisticshttps://danmackinlay.name/notebook/algorithmic_statistics.htmlSun, 26 Sep 2021 09:11:03 +1000https://danmackinlay.name/notebook/algorithmic_statistics.htmlEmpirical estimation of computation Information-based complexity theory References The intersection between probability, ignorance, and algorithms, butting up against computational complexity, coding theory, dynamical systems, ergodic theory, minimum description length and probability. Random number generation relates here, too.
When is the relation between things sufficiently jointly unstructured that we may treat them as random? Stochastic approximations to deterministic algorithms. Kolmogorov complexity. Compressibility, Shannon information. Sideswipe at deterministic chaos.Combinatorics of notehttps://danmackinlay.name/notebook/combinatorics.htmlSat, 18 Jul 2020 12:25:14 +1000https://danmackinlay.name/notebook/combinatorics.html Algorithmic complexity and quasi monte carlo both consider combinatorial matters too.
Jörg Arndt’s Matters ComputationalThis is a simulationhttps://danmackinlay.name/notebook/this_is_a_simulation.htmlTue, 04 Apr 2017 20:36:28 +1000https://danmackinlay.name/notebook/this_is_a_simulation.htmlReferences Bekenstein limits, (quantum) information theory and physics, dreams in the minds of gods etc…
Scott Aaronson, Your yearly dose of is-the-universe-a-simulation:
[…] to whatever extent we believe the Bekenstein bound […] we believe that in quantum gravity, any bounded physical system (with a short-wavelength cutoff, yada yada) lives in a Hilbert space of a finite number of qubits, perhaps \(~10^69\) qubits per square meter of surface area.Quasi Monte Carlohttps://danmackinlay.name/notebook/qmc.htmlTue, 14 Feb 2017 11:48:36 +1100https://danmackinlay.name/notebook/qmc.htmlReferences Simplistically put, using a random, Monte Carlo style algorithm, but deterministically, by sampling at well-chosen points.
Key words: discrepancy.
Some of the series of points used are nice for parallelised algorithms, by the way, in the same way that randomised algorithms are.
Low discrepancy sequences such as Sobol nets, others? Do Gray codes, fit in here? If you aren’t doing this incrementally you can pre-generate a point set rather than a sequence.Inference from disorderhttps://danmackinlay.name/notebook/inference_from_disorder.htmlWed, 19 Oct 2016 14:44:59 +1100https://danmackinlay.name/notebook/inference_from_disorder.htmlReferences Placeholder.
I don’t know if this is a real category, but between conversations with Jonas Peters, Aurora Delaigle and Zdravko Botev, I’ve seen a few references to the idea that we can draw inference from the lack of structure, in some sense, of the world.
Janzing and Peters and so forth do this with inferring the arrow of time or causality. Delaigle and Hall do very blind statistical deconvolution.Random number generationhttps://danmackinlay.name/notebook/prng.htmlTue, 13 Oct 2015 12:36:18 +0800https://danmackinlay.name/notebook/prng.htmlUniform PRNGs Non-uniform RNG algorithms Practical pseudo-RNG implementation. See also pseudorandomness for theories Monte Carlo for some applications, and for some background theory algorithmic statistics.
Uniform PRNGs Generating uniformly distributed numbers on some interval, such as [0,1].
I constantly have to do this in languages that do not conveniently support…
local
seedable
convenient
… PRNGs.
Javascript doesn’t support seeding. Supercollider does but insists on a per-thread RNG.Computational mechanicshttps://danmackinlay.name/notebook/computational_mechanics.htmlFri, 02 Jan 2015 20:33:58 +0100https://danmackinlay.name/notebook/computational_mechanics.htmlTo read To understand To read Decisional states
“This article introduces both a new algorithm for reconstructing epsilon-machines from data, as well as the decisional states. These are defined as the internal states of a system that lead to the same decision, based on a user-provided utility or pay-off function.”
CRS’s CSSR
To understand Are there actual applications of this to actual physics, or is this keyword purely the mule offspring of physics and computer science?Computational complexityhttps://danmackinlay.name/notebook/computational_complexity.htmlFri, 17 Oct 2014 05:21:09 +0000https://danmackinlay.name/notebook/computational_complexity.html References Not my area, but I should note my favourite “wow, cool” readings somewhere.
NP-complete Problems and Physical Reality Hector Zainil’s various projects What actually would simulating the whole world entail? References Roughgarden, Tim. 2018. “Complexity Theory, Game Theory, and Economics.” arXiv:1801.00734 [Cs, Econ], January.