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bayes.txt
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* http://www.ams.org/notices/200601/rev-faris.pdf
* http://www.stat.uchicago.edu/~lekheng/courses/191f09/mumford-AMS.pdf
* http://www.randomhacks.net/articles/2007/02/22/bayes-rule-and-drug-tests
* http://www.cs.princeton.edu/courses/archive/spr06/cos598C/papers/AndrieuFreitasDoucetJordan2003.pdf
MCMC
* Hat Gibbs-Sampling etwas mit dem randomisierten Lanczos/Krasevacz/Kaczmarz (wie hieß
der?) zu tun? Siehe Seite 22 von
http://www.cs.princeton.edu/courses/archive/spr06/cos598C/papers/AndrieuFreitasDoucetJordan2003.pdf.
* http://en.wikipedia.org/wiki/Bertrand_paradox_(probability)
* http://brian.weatherson.org/conprob.pdf
Intuitionistische Wahrscheinlichkeitstheorie
* http://link.springer.com/article/10.1007%2Fs10485-013-9324-9
A Categorical Foundation for Bayesian Probability
* http://www.mth.kcl.ac.uk/~streater/EPR.html
We show that the postulate of von Neumann, that on measurement the wave
function collapses to an eigenstate of the observable being measured, follows
from Bayes's rule for conditioning probabilities in classical probability.
* Student von John Baez:
http://www.cs.ox.ac.uk/bob.coecke/Brendan_Fong.pdf
"My student Brendan Fong wrote a masters' thesis about Bayesian networks,
which he's trying to polish up and publish.
In the new improved version, he'll associate to any Bayesian network a
category with finite products, say T. This plays the role of a "theory".
An assignment of probabilities to random variables consistent with this
theory is a symmetric monoidal functor from T to C, where C is some
symmetric monoidal category - but not cartesian! - category of probability
measure spaces and stochastic maps. So, [T,C] plays the role of the
"category of models of T in C"."
* Why I am only a half-Bayesian.
http://ftp.cs.ucla.edu/pub/stat_ser/r284-reprint.pdf
* Interview with Will Kurt on his latest book: Bayesian Statistics The Fun Way
=== Markov-Ketten
* Eine Markov-Kette ist eine Koalgebra für den Funktor X |-> D(X),
Menge der Wahrscheinlichkeitsverteilungen auf X.
* Wenn man die Größe einer Menge Omega kennt und auf ihr eine Dichte
kennt, die man auswerten und aus der man ziehen kann, dann kann man die Größe
von Teilmengen als geeigneten Erwartungswert schreiben.
=== Grundlegung über Zufallsvariablen statt Mengen
https://golem.ph.utexas.edu/category/2007/02/category_theoretic_probability.html
http://www.stat.uchicago.edu/~lekheng/courses/191f09/mumford-AMS.pdf
David Mumford (yes, that one) has an interesting paper about refounding
mathematics using probability measures rather than sets. (Experts will surely
complain about my phrasing there; see the paper for yourself.) Apparently it
settles the Continuum Hypothesis and the Axiom of Choice.
=== Bayes-Update
* Es gibt einen subtilen Unterschied zwischen "X" und "Ich beobachte X".
https://www.lesswrong.com/posts/CvKnhXTu9BPcdKE4W/an-untrollable-mathematician-illustrated
=== Bedingte Unabhängigkeit kategoriell
http://homepages.inf.ed.ac.uk/als/Talks/tacl13.pdf
Eine gute Kategorie von Wahrscheinlichkeitsräumen: https://arxiv.org/pdf/1701.02547.pdf
=== Verteilte Bayessche Interferenz
http://arxiv.org/abs/1608.01987
=== Logische Induktion
https://arxiv.org/abs/1609.03543
http://www.ejwagenmakers.com/2016/RouderEtAl2016FreeLunch.pdf
=== Vortrag, der gut sein könnte
http://videolectures.net/site/normal_dl/tag=50814/mlss09uk_jordan_bfway.pdf