A characterization of partition polynomials and good Bernoulli trial measures in many symbols

Andrew Yingst. "A characterization of partition polynomials and good Bernoulli trial measures in many symbols." Colloquium Mathematicae 135.2 (2014): 263-293. <http://eudml.org/doc/284309>.

@article{AndrewYingst2014,
abstract = {Consider an experiment with d+1 possible outcomes, d of which occur with probabilities $x₁,..., x_\{d\}$. If we consider a large number of independent occurrences of this experiment, the probability of any event in the resulting space is a polynomial in $x₁,..., x_\{d\}$. We characterize those polynomials which arise as the probability of such an event. We use this to characterize those x⃗ for which the measure resulting from an infinite sequence of such trials is good in the sense of Akin.},
author = {Andrew Yingst},
journal = {Colloquium Mathematicae},
keywords = {good measures in the sense of Akin; Cantor space; partition polynomials; Bernoulli trial measures},
language = {eng},
number = {2},
pages = {263-293},
title = {A characterization of partition polynomials and good Bernoulli trial measures in many symbols},
url = {http://eudml.org/doc/284309},
volume = {135},
year = {2014},
}

TY - JOUR
AU - Andrew Yingst
TI - A characterization of partition polynomials and good Bernoulli trial measures in many symbols
JO - Colloquium Mathematicae
PY - 2014
VL - 135
IS - 2
SP - 263
EP - 293
AB - Consider an experiment with d+1 possible outcomes, d of which occur with probabilities $x₁,..., x_{d}$. If we consider a large number of independent occurrences of this experiment, the probability of any event in the resulting space is a polynomial in $x₁,..., x_{d}$. We characterize those polynomials which arise as the probability of such an event. We use this to characterize those x⃗ for which the measure resulting from an infinite sequence of such trials is good in the sense of Akin.
LA - eng
KW - good measures in the sense of Akin; Cantor space; partition polynomials; Bernoulli trial measures
UR - http://eudml.org/doc/284309
ER -