Results 11 to 20 of about 527,420 (298)
Bayesian nonparametric adjustment of confounding. [PDF]
Biometrics, 2023 AbstractAnalysis of observational studies increasingly confronts the challenge of determining which of a possibly high-dimensional set of available covariates are required to satisfy the assumption of ignorable treatment assignment for estimation of causal effects.
Kim C, Tec M, Zigler C.
europepmc +4 more sources
Bayesian Nonparametric Monotone Regression. [PDF]
Environmetrics, 2020 AbstractIn many applications there is interest in estimating the relation between a predictor and an outcome when the relation is known to be monotone or otherwise constrained due to the physical processes involved. We consider one such application‐inferring time‐resolved aerosol concentration from a low‐cost differential pressure sensor. The objective
Wilson A +3 more
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A Bayesian nonparametric estimation to entropy [PDF]
Brazilian Journal of Probability and Statistics, 2021 A Bayesian nonparametric estimator to entropy is proposed. The derivation of the new estimator relies on using the Dirichlet process and adapting the well-known frequentist estimators of Vasicek (1976) and Ebrahimi, Pflughoeft and Soofi (1994). Several theoretical properties, such as consistency, of the proposed estimator are obtained.
Al-Labadi, Luai +3 more
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Discussion of “Nonparametric Bayesian Inference in Applications”: Bayesian nonparametric methods in econometrics [PDF]
Statistical Methods & Applications, 2017 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jim E. Griffin +2 more
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Nonparametric Bayesian Image Segmentation [PDF]
International Journal of Computer Vision, 2007 Image segmentation algorithms partition the set of pixels of an image into a specific number of different, spatially homogeneous groups. We propose a nonparametric Bayesian model for histogram clustering which automatically determines the number of segments when spatial smoothness constraints on the class assignments are enforced by a Markov Random ...
Orbanz, Peter, Buhmann, Joachim M.
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Semiparametric Bayesian inference in multiple equation models [PDF]
, 2005 This paper outlines an approach to Bayesian semiparametric regression in multiple equation models which can be used to carry out inference in seemingly unrelated regressions or simultaneous equations models with nonparametric components.
Koop, G.M. +5 more
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Bayesian nonparametric subspace estimation [PDF]
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017 Principal component analysis is a widely used technique to perform dimension reduction. However, selecting a finite number of significant components is essential and remains a crucial issue. Only few attempts have proposed a probabilistic approach to adaptively select this number. This paper introduces a Bayesian nonparametric model to jointly estimate
Elvira, Clément +2 more
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Bayesian Nonparametric Inference for a Multivariate Copula Function [PDF]
, 2014 The paper presents a general Bayesian nonparametric approach for estimating a high dimensional copula. We first introduce the skew-normal copula, which we then extend to an infinite mixture model.
Wu, Juan, Wang, Xue, Walker, Stephen G.
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Bayesian nonparametric estimators derived from conditional Gibbs structures [PDF]
, 2008 We consider discrete nonparametric priors which induce Gibbs-type exchangeable random partitions and investigate their posterior behavior in detail.
PRUENSTER, IGOR +5 more
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CoRR, 2012 The Bayesian Logic (BLOG) language was recently developed for defining first-order probability models over worlds with unknown numbers of objects. It handles important problems in AI, including data association and population estimation. This paper extends BLOG by adopting generative processes over function spaces - known as nonparametrics in the ...
Carbonetto, P +3 more
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