Suchergebnisse - "MCMC sampling algorithm"

Nonparametric function estimation subject to monotonicity, convexity and other shape constraints von Shively, Thomas S., Walker, Stephen G., Damien, Paul

“… A computationally efficient MCMC sampling algorithm is developed that converges faster than previous methods for non-Gaussian models …”
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Bayesian flexible hierarchical skew heavy-tailed multivariate meta regression models for individual patient data with applications von Kim, Sungduk, Chen, Ming-Hui, Ibrahim, Joseph, Shah, Arvind, Lin, Jianxin

“… ), High Density Lipoprotein Cholesterol (HDL-C), and Triglycerides (TG). These three continuous outcome measures are correlated and shed much light on a subject's lipid status …”
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Bayesian Modeling on Microbiome Data Analysis: Application to Subgingival Microbiome Study von Gwon, Yeongjin, Yu, Fang, Payne, Jeffrey B., Mikuls, Ted R.

“… ) regression model with random subject effects. This proposed approach not only accommodates inflated …”
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Ultra-Efficient MCMC for Bayesian Longitudinal Functional Data Analysis von Sun, Thomas Y., Kowal, Daniel R.

“… Using a novel blocking structure paired with an orthogonalized basis reparameterization, our algorithm jointly samples the fixed effects regression functions together with all subject- and replicate …”
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Semiparametric GARCH via Bayesian Model Averaging von Wilson Ye Chen, Richard H. Gerlach

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Ultra-efficient MCMC for Bayesian longitudinal functional data analysis von Sun, Thomas Y, Kowal, Daniel R

“… Using a novel blocking structure paired with an orthogonalized basis reparametrization, our algorithm jointly samples the fixed effects regression functions together with all subject- and replicate …”
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Semiparametric GARCH via Bayesian model averaging von Wilson Ye Chen, Gerlach, Richard H

“… As the dynamic structure of the financial markets is subject to dramatic changes, a model capable of providing consistently accurate volatility estimates must not make strong assumptions on how prices change over time …”
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Semiparametric GARCH via Bayesian model averaging von Wilson Ye Chen, Gerlach, Richard H

“… As the dynamic structure of the financial markets is subject to dramatic changes, a model capable of providing consistently accurate volatility estimates must not make strong assumptions on how prices change over time …”
Volltext