Adaptive rates of contraction of posterior distributions in Bayesian wavelet regression

2014 ◽  
Vol 145 ◽  
pp. 92-101 ◽  
Author(s):  
Heng Lian
Keyword(s):  
Posterior Distributions ◽  
Wavelet Regression ◽  
Bayesian Wavelet

Bayesian Wavelet Regression for Spatial Estimation

2021 ◽  
Vol 6 (2) ◽  
pp. 219-229
Author(s):  
G. Avarez ◽  
B. Sans´o
Keyword(s):  
Spatial Estimation ◽  
Wavelet Regression ◽  
Bayesian Wavelet

Bayesian Wavelet Regression for Spatial Estimation

2021 ◽  
Vol 6 (3) ◽  
pp. 219-229
Author(s):  
G. Avarez ◽  
B. Sans´o
Keyword(s):  
Spatial Estimation ◽  
Wavelet Regression ◽  
Bayesian Wavelet

scholarly journals Genetic parameters for test-day model with random regressions for production traits of Czech Holstein cattle

2011 ◽  
Vol 50 (No. 4) ◽  
pp. 142-154 ◽  
Author(s):  
L. Zavadilová ◽  
J. Jamrozik ◽  
Schaeffer LR
Keyword(s):  
Milk Fat ◽  
Sampling Method ◽  
Random Regression ◽  
Model Parameters ◽  
Residual Variance ◽  
Posterior Distributions ◽  
Production Traits ◽  
Multiple Trait ◽  
Holstein Breed ◽  
Random Regression Model

Multiple-lactation random regression model was applied to test-day records of milk, fat and protein yields in the first three lactations of the Czech Holstein breed. Data included 9 583 cows, 89 584, 44 207 and 11 266 test-day records in the first, second and third lactation, respectively. Milk, fat and protein in the first three lactations were analysed separately and in a multiple-trait analysis. Linear model included herd-test date, fixed regressions within age-season class and two random effects: animal genetic and permanent environment modelled by regressions. Gibbs sampling method was used to generate samples from marginal posterior distributions of the model parameters. The single- and multiple-trait models provided similar results. Genetic and permanent environmental variances and heritability for particular days in milk were high at the beginning and at the end of lactation. The residual variance decreased throughout the lactation. The resulting heritability ranged from 0.13 to 0.52 and increased with parity.  

scholarly journals Parallel Markov chain Monte Carlo for non-Gaussian posterior distributions

Stat ◽  
2015 ◽  
Vol 4 (1) ◽  
pp. 304-319 ◽  
Author(s):  
Alexey Miroshnikov ◽  
Zheng Wei ◽  
Erin Marie Conlon
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Posterior Distributions ◽  
Non Gaussian

scholarly journals Rates of contraction of posterior distributions based on Gaussian process priors

2008 ◽  
Vol 36 (3) ◽  
pp. 1435-1463 ◽  
Author(s):  
A. W. van der Vaart ◽  
J. H. van Zanten
Keyword(s):  
Gaussian Process ◽  
Posterior Distributions
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