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Trend filtering uses the generalized lasso framework to fit an adaptive polynomial of degree k to estimate the function f_0 at each input x_i in the model: y_i = f_0(x_i) + epsilon_i, for i = 1, ..., n, and epsilon_i is sub-Gaussian with E(epsilon_i) = 0. Bayesian trend filtering adapts the genlasso framework to a fully Bayesian hierarchical model, estimating the penalty parameter lambda within a tractable Gibbs sampler.
Package details |
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Author | Edward A. Roualdes |
Maintainer | Edward A. Roualdes <eroualdes@csuchico.edu> |
License | GPL (>= 2.0) |
Version | 1.2 |
Package repository | View on CRAN |
Installation |
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