Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Bayesian Dyadic Trees and Histograms for Regression
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a complexity prior on the number of steps, πK(k) ∝ exp(-cK k log k), with equivalent blocks | Bayesian equivalent-block regression histograms · Piecewise-constant regression | posterior concentration rate: n^{-1/2}\sqrt{K_{f_0}\log(n/K_{f_0})} | Helped |
| Use balanced intervals with a uniform prior over balanced partitions and the complexity prior on the number of steps | Bayesian balanced-interval regression histograms · Piecewise-constant regression | posterior concentration rate: n^{-1/2}\sqrt{K_{f_0}\log^{2\beta}(n/K_{f_0})} | Mixed |
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