Zobrazeno 1 - 10
of 350
pro vyhledávání: '"non parametric Bayesian inference"'
Partial orders may be used for modeling and summarising ranking data when the underlying order relations are less strict than a total order. They are a natural choice when the data are lists recording individuals' positions in queues in which queue o
Externí odkaz:
http://arxiv.org/abs/2408.14661
Publikováno v:
RESEARCHERS.ONE (2019), https://www.researchers.one/article/2019-06-6
We study the problem of non-parametric Bayesian estimation of the intensity function of a Poisson point process. The observations are $n$ independent realisations of a Poisson point process on the interval $[0,T]$. We propose two related approaches.
Externí odkaz:
http://arxiv.org/abs/1804.03616
Publikováno v:
The Econometrics Journal, 2018 Jan 01. 21(3), 298-315.
Externí odkaz:
https://www.jstor.org/stable/45172285
Publikováno v:
Healthcare Technology Letters, Vol 8, Iss 2, Pp 25-30 (2021)
Abstract The rapid proliferation of wearable devices for medical applications has necessitated the need for automated algorithms to provide labelling of physiological time‐series data to identify abnormal morphology. However, such algorithms are le
Externí odkaz:
https://doaj.org/article/910318a131ae4154abf0cb7d8749da3c
Autor:
Bathaee, Najmeh, Sheikhzadeh, Hamid
Publikováno v:
In Statistical Methodology December 2016 33:256-275
Autor:
Hutter, Marcus
Given i.i.d. data from an unknown distribution, we consider the problem of predicting future items. An adaptive way to estimate the probability density is to recursively subdivide the domain to an appropriate data-dependent granularity. A Bayesian wo
Externí odkaz:
http://arxiv.org/abs/0903.5342
Publikováno v:
Journal of the Royal Statistical Society. Series B (Statistical Methodology), 2011 Jun 01. 73(3), 377-406.
Externí odkaz:
https://www.jstor.org/stable/41262676
Autor:
ERHARDSSON, TORKEL
Publikováno v:
Scandinavian Journal of Statistics, 2008 Jun 01. 35(2), 369-384.
Externí odkaz:
https://www.jstor.org/stable/41548599
Akademický článek
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Autor:
Hutter, Marcus
Publikováno v:
Proc. 10th International Conf. on Artificial Intelligence and Statistics (AISTATS-2005) 144-151
Given i.i.d. data from an unknown distribution, we consider the problem of predicting future items. An adaptive way to estimate the probability density is to recursively subdivide the domain to an appropriate data-dependent granularity. A Bayesian wo
Externí odkaz:
http://arxiv.org/abs/math/0411515