2 code implementations • 17 May 2023 • Evelyn J. Mannix, Howard D. Bondell
In many machine learning applications, labeling datasets can be an arduous and time-consuming task.
no code implementations • 21 Oct 2020 • Yiping Guo, Howard D. Bondell
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.
no code implementations • 21 Oct 2020 • Yiping Guo, Howard D. Bondell
Probabilistic principal component analysis (PPCA) is a probabilistic reformulation of principal component analysis (PCA), under the framework of a Gaussian latent variable model.
no code implementations • 17 Aug 2020 • David B. Huberman, Brian J. Reich, Howard D. Bondell
We propose a technique that simultaneously estimates the entire conditional distribution and flexibly allows for machine learning techniques to be incorporated.
1 code implementation • 14 Mar 2019 • Rui Li, Howard D. Bondell, Brian J. Reich
Due to their flexibility and predictive performance, machine-learning based regression methods have become an important tool for predictive modeling and forecasting.
1 code implementation • 31 Aug 2016 • Yan Dora Zhang, Brian P. Naughton, Howard D. Bondell, Brian J. Reich
The proposed method compares favourably to previous approaches in terms of both concentration around the origin and tail behavior, which leads to improved performance both in posterior contraction and in empirical performance.
Methodology