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pro vyhledávání: '"Bondell, Howard D."'
Autor:
Mannix, Evelyn J., Bondell, Howard D.
In many machine learning applications, labeling datasets can be an arduous and time-consuming task. Although research has shown that semi-supervised learning techniques can achieve high accuracy with very few labels within the field of computer visio
Externí odkaz:
http://arxiv.org/abs/2305.10071
Spatially dependent data arises in many applications, and Gaussian processes are a popular modelling choice for these scenarios. While Bayesian analyses of these problems have proven to be successful, selecting prior distributions for these complex m
Externí odkaz:
http://arxiv.org/abs/2301.09951
In Bayesian analysis, the selection of a prior distribution is typically done by considering each parameter in the model. While this can be convenient, in many scenarios it may be desirable to place a prior on a summary measure of the model instead.
Externí odkaz:
http://arxiv.org/abs/2111.10718
Autor:
Guo, Yiping, Bondell, Howard D.
Compared to the conditional mean as a simple point estimator, the conditional density function is more informative to describe the distributions with multi-modality, asymmetry or heteroskedasticity. In this paper, we propose a novel parametric condit
Externí odkaz:
http://arxiv.org/abs/2010.10896
Autor:
Guo, Yiping, Bondell, Howard D.
Probabilistic principal component analysis (PPCA) is a probabilistic reformulation of principal component analysis (PCA), under the framework of a Gaussian latent variable model. To improve the robustness of PPCA, it has been proposed to change the u
Externí odkaz:
http://arxiv.org/abs/2010.10786
Nonparametric Conditional Density Estimation In A Deep Learning Framework For Short-Term Forecasting
Short-term forecasting is an important tool in understanding environmental processes. In this paper, we incorporate machine learning algorithms into a conditional distribution estimator for the purposes of forecasting tropical cyclone intensity. Many
Externí odkaz:
http://arxiv.org/abs/2008.07653
Due to their flexibility and predictive performance, machine-learning based regression methods have become an important tool for predictive modeling and forecasting. However, most methods focus on estimating the conditional mean or specific quantiles
Externí odkaz:
http://arxiv.org/abs/1903.06023
Akademický článek
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Autor:
Yu, Weichang1 (AUTHOR) howard.bondell@unimelb.edu.au, Bondell, Howard D.1 (AUTHOR)
Publikováno v:
Journal of the American Statistical Association. Jun2024, Vol. 119 Issue 546, p1089-1101. 13p.
Prior distributions for high-dimensional linear regression require specifying a joint distribution for the unobserved regression coefficients, which is inherently difficult. We instead propose a new class of shrinkage priors for linear regression via
Externí odkaz:
http://arxiv.org/abs/1609.00046