Comparison of Two Methods for Calculating the Partition Functions of Various Spatial Statistical Models
Comparison of Two Methods for Calculating the Partition Functions of Various Spatial Statistical Models
复制标题
两种空间统计模型配分函数计算方法的比较
DOI：
10.1111/1467-842x.00154
复制
发表时间：
2001
影响因子：
1.1
通讯作者：
Y. Ogata
中科院分区：
文献类型：
--
作者：
F. Huang;Y. Ogata
空间统计中的似然计算需要准确有效地计算模型吉布斯分布的归一化常数（即配分函数）。通过伊辛模型、高斯马尔可夫场模型和成对相互作用点场模型的仿真实验，对两种可用的马尔可夫链蒙特卡罗方法计算归一化常数的方法进行了比较。
Likelihood computation in spatial statistics requires accurate and efficient calculation of the normalizing constant (i.e. partition function) of the Gibbs distribution of the model. Two available methods to calculate the normalizing constant by Markov chain Monte Carlo methods are compared by simulation experiments for an Ising model, a Gaussian Markov field model and a pairwise interaction point field model.