Castillo, I., Schmidt-Hieber, J., van der Vaart, A., 2015. Bayesian linear regression with sparse priors. The Annals of Statistics 43 (4), 1986-2018.Castillo I, Schmidt-Hieber J, van der Vaart A. Bayesian linear regression with sparse priors. Annals of statistics. 2015 Oct;43(5):...
Castillo, I., Schmidt-Hieber, J., Van der Vaart, A.: Bayesian linear regression with sparse priors. Ann. Stat. 43, 1986–2018 (2015) Article MathSciNet MATH Google Scholar Cefalu, M., Dominici, F., Arvold, N., Parmigiani, G.: Model averaged double robust estimation. Biometrics 73...
Bayesian linear regression model with semiconjugate priors for stochastic search variable selection (SSVS) expand all in page Description TheBayesian linear regression modelobjectmixsemiconjugateblmspecifies the joint prior distribution of the regression coefficients and the disturbance variance (β,σ2) for...
In the Using ridge regression to overcome linear regression's shortfalls recipe, we discussed the connections between the constraints imposed by ridge regression from an optimization standpoint. We also discussed the Bayesian interpretation of priors on the coefficients, which attract the mass of the d...
(2012) for default priors in Bayesian linear regression, and see Ly, Verhagen, and Wagenmakers, (2016) for a general introduction on default Bayes factor hypothesis tests). An example of such a desideratum is that the Bayes factor is the same regardless of the units of measurement (e.g....
Quick start Bayesian linear regression of y on x, using default normal priors for the regression coefficients and an inverse-gamma prior for the variance bayes: regress y x Same as above, but use a standard deviation of 10 instead of 100 for the default normal priors and shape of 2 and ...
In the most thoroughly analyzed regression problem, the best models were those with less restrictive priors. This emphasizes the major advantage of the Bayesian approach, that we are not forced to guess attributes that are unknown, such as the number of degrees of freedom in the model, non-...
Quick start Bayesian heteroskedastic linear regression of y on x1 and x2, using z1 to model the variance and using default normal priors for regression coefficients and log-variance coefficients bayes: hetregress y x1 x2, het(z1) Use a standard deviation of 10 instead of 100 for the ...
Bayesian sparse factor analysis with kernelized observations 2022, Neurocomputing Citation Excerpt : Among their many variants, such as Probabilistic PCA [11], Supervised PCA [12], Bayesian Factor Regression [13] or Bayesian CCA [14], the Inter-Battery FA model [14] stands out for its capabilit...
Priors Bayesian Workflow Bayesian Linear Regression Bayesian Logistic Regression Bayesian Ordinal Regression Bayesian Regression with Count Data: Poisson Regression Robust Bayesian Regression Bayesian Sparse Regression Hierarchical Models Markov Chain Monte Carlo (MCMC) and Model Metrics ...