polycyclic aromatic hydrocarbonsSPERM QUALITYMETABOLITESTOXICITYACTIVATIONDAMAGEThis study aimed to assess mixture effects of 16 targeted PAHs on male reproductive health by applying a novel grouping approach to
is a Bayesian semi-parametric generalized linear model approach under identity and probit links.There are a number of functions in this package that extend Bayesian kernel machine regressionfits to allow multiple-chain inference and diagnostics,which leverage functions from the'future','rstan',and'...
The R package bkmr implements Bayesian kernel machine regression, a statistical approach for estimating the joint health effects of multiple concurrent exposures. Additional information on the statistical methodology and on the computational details are provided in Bobb et al. 2015. More recent extensions...
Single and mixed effects of these elements on NTD risk were evaluated with Bayesian kernel machine regression, and the effects of individual elements were validated using logistic regression. As a result, NTD risk increased with the concentration of the mixture of the 10 elements. NTD risk rose ...
Bayesian Kernel Machine Regression BMI: Body mass index CI: Confidence Interval EDCs: Endocrine disrupting chemicals ER + : Estrogen Receptor positive ER-: Estrogen Receptor negative FFTP: First full-term pregnancy IARC: International Agency for Research on Cancer LOD: Limit of detec...
For details on the analytically tractable posterior distributions offered by the Bayesian linear regression model framework in Econometrics Toolbox, see Analytically Tractable Posteriors. Otherwise, you must use numerical integration techniques to compute integrals of h(β,σ2) with respect to posterior ...
For convenience, and according to the usual convention in machine learning, we will still refer to this as a linear model. In a regression model, we assume that the noise is normally distributed around a mean predicted by the model so that for training data pair $\left\{\mathbf{x}_{i}...
In this paper we introduce Bayesian nonparmetric kernel (BaNK) learning, a generic, data-driven framework for scalable learning of kernels. We show that this framework can be used for performing both regression and classification tasks and scale to large datasets. Furthermore, we show that BaNK...
4 The Relevance Vector Machine in Action 4.1 Illustrative synthetic data: regression The function sinc(x) = sin(x)/x has been a popular choice to illustrate support vector regres- sion (Vapnik, Golowich, and Smola 1997; Vapnik 1998), where in place of the classification margin, the -...
Spectrum dependent learning curves in kernel regression and wide neural networks. In Proc. 37th International Conference on Machine Learning (eds Daumé, H. III & Singh, A.) 1024–1034 (PMLR, 2020). Dietrich, R., Opper, M. & Sompolinsky, H. Statistical mechanics of support vector networks...