维纳过程单变点模型的贝叶斯参数估计

Bayesian Parameter Estimation of Wiener Process with a Change-Point

通过引入潜在变量,利用正态分布的重要性质得到了维纳过程单变点模型比较简单的似然函数.结合Metropolis-Hastings算法对参数进行Gibbs抽样,基于Gibbs样本对参数进行估计.随机模拟的结果表明估计的精度较高.

By introducing a latent variable, the simple likelihood function of Wiener process with a change-point is obtained according to the important property of the normal distribution. All the parameters are sampled by Gibbs sampler together with Metropolis-Hastings algorithm, and the parameters are estimated based on the Gibbs samples. Random simulation results show that the estimations are fairly accurate.