Sequential Bayesian computation of logistic regression models
Sequential Bayesian computation of logistic regression models
复制标题
逻辑回归模型的顺序贝叶斯计算
DOI：
10.1109/icassp.1999.759927
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发表时间：
1999
期刊：
影响因子：
--
通讯作者：
M. Niranjan
中科院分区：
文献类型：
--
作者：
M. Niranjan
扩展卡尔曼滤波（EKF）算法的状态空间模型的识别被证明是一个明智的工具，估计逻辑回归模型顺序。逻辑模型的参数上的高斯概率密度在逐个样本的基础上传播。另外两种方法，拉普拉斯近似和变分近似的状态空间制定进行了比较。后一种方法的特点，如通过最大化的“创新概率”推断噪声水平的可能性表示。这些想法的合成问题和两个真实的世界问题的实验插图进行了讨论。
The extended Kalman filter (EKF) algorithm for identification of a state space model is shown to be a sensible tool in estimating a logistic regression model sequentially. A Gaussian probability density over the parameters of the logistic model is propagated on a sample by sample basis. Two other approaches, the Laplace approximation and the variational approximation are compared with the state space formulation. Features of the latter approach, such as the possibility of inferring noise levels by maximising the "innovation probability" are indicated. Experimental illustrations of these ideas on a synthetic problem and two real world problems are discussed.