Bayesian curve-fitting with free-knot splines
Bayesian curve-fitting with free-knot splines
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DOI：
10.1093/biomet/88.4.1055
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发表时间：
2001-12-01
期刊：
影响因子：
2.7
通讯作者：
Kass, RE
中科院分区：
文献类型：
--
作者：
DiMatteo, I;Genovese, CR;Kass, RE
我们描述了一种贝叶斯方法，用于将曲线拟合到从指数族中提取的数据，该方法使用样条线，其中结的数量和位置是自由参数。该方法使用可逆跳跃马尔可夫链蒙特卡罗来改变结配置和局部启发式来加速混合。对于非正态模型，我们在先验条件下使用贝叶斯信息准则 BIC 来近似计算接受概率所需的积分似然比，从而使该近似变得准确。我们的技术基于结数量和位置上的边缘化链，但我们提供了在正态和非正态模型中推断回归系数及其函数的方法。模拟结果表明该方法表现良好，我们在两个神经科学应用中说明了该方法。
We describe a Bayesian method, for fitting curves to data drawn from an exponential family, that uses splines for which the number and locations of knots are free parameters. The method uses reversible-jump Markov chain Monte Carlo to change the knot configurations and a locality heuristic to speed up mixing. For nonnormal models, we approximate the integrated likelihood ratios needed to compute acceptance probabilities by using the Bayesian information criterion, BIC, under priors that make this approximation accurate. Our technique is based on a marginalised chain on the knot number and locations, but we provide methods for inference about the regression coefficients, and functions of them, in both normal and nonnormal models. Simulation results suggest that the method performs well, and we illustrate the method in two neuroscience applications.