Appendix B. Algorithms for Gibbs sampling for a growth model.

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贝叶斯统计

生长模型参数

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Algorithms for Gibbs sampling for a growth model.

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2016-08-04

Replication data for: Improving Predictions Using Ensemble Bayesian Model Averaging

预测模型

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We present ensemble Bayesian model averaging (EBMA) and illustrate its ability to aid scholars in the social sciences to make more accurate forecasts of future events. In essence, EBMA improves predic

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We show that lower-dimensional marginal densities of dependent zero-mean normal distributions truncated to the positive orthant exhibit a mass-shifting phenomenon. Despite the truncated multiva

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The total-evidence approach to divergence time dating uses molecular and morphological data from extant and fossil species to infer phylogenetic relationships, species divergence times, and macroevo

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生物振荡

Understanding the oscillating behaviors that govern organisms’ internal biological processes requires interdisciplinary efforts combining both biological and computer experiments, as the latter can co

DataCite Commons2026-01-26 更新90

FIG_S2_BAMM_sensitivity_to_prior

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Figure S2. Prior and posterior distributions of the number of rate shifts based on priors of 1, 5, and 10 shifts. Although the distribution of the number of shifts in the posterior is sensitive to the

DataONE2017-09-05 更新80

Appendix B. Algorithms for Gibbs sampling for a growth model.

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