Ӧ�ø���ͳ�� 2008, 24(3) 312-318 DOI:      ISSN: 1001-4268 CN: 31-1256

����Ŀ¼ | ����Ŀ¼ | ������� | �߼�����                                                            [��ӡ��ҳ]   [�ر�]
Supporting info
Email Alert
Article by
Monte Carlo EM�����㷨
����ʦ����ѧ������ͳ��ѧԺ, �㽭�ƾ�ѧԺ��ѧ��ͳ��ѧԺ
ժҪ�� EM�㷨�ǽ��������õ�����������Ĺ��Ƶ�һ�����������㷨, �����������E���л��ֵ���ʾ���ʽ��ʱ������, ����������, ��������Ӧ�õĹ㷺��. ��Monte Carlo EM�㷨�ܺõؽ�����������, ��EM�㷨��E���Ļ�����Monte Carloģ������Чʵ��, ʹ�������Դ����ǿ. ��������EM�㷨, ����Monte Carlo EM�㷨, �������ٶȶ������Ե�, ��ȱ����Ϣ�ĵ���������, ��ȱ�����ݵı����ܸ�ʱ, �����ٶȾͷdz�����. ��Newton-Raphson�㷨�ں��������ĸ������ж�����������. �������Monte Carlo EM�����㷨, ��Monte Carlo EM�㷨��Newton-Raphson�㷨���, ��ʹ��EM�㷨�е�E����Monte Carloģ�����ʵ��, ��֤���˸��㷨�ں��������������ж��������ٶ�. �Ӷ�ʹ�䱣����Monte Carlo EM�㷨���ŵ�, ���Ľ���Monte Carlo EM�㷨�������ٶ�. ����ͨ����ֵ����, ��Monte Carlo EM�����㷨�Ľ����EM�㷨��Monte Carlo EM�㷨�Ľ�����бȽ�, ��һ��˵����Monte Carlo EM�����㷨��������.
�ؼ����� ��������   Monte   Carloģ��   EM�㷨   Monte   Carlo   EM�㷨   Newton-Raphson�㷨.  
Acceleration of Monte Carlo EM Algorithm
Luo Ji
School of Finance and Statistics, East China Normal University; School of Mathematics and Statistics, Zhejiang University ofFinance and Economics
Abstract: EM algorithm is one of the data augmentation algorithms, which usually are used to obtain estimate of the posterior mode of observed data recent years. However, because of its difficulty in calculating the explicit expression of the integral in E step, the application of EM algorithm is limited. While Monte Carlo EM algorithm solves the problem well. Owing to effectively facilitating the integral in E step of EM algorithm by Monte Carlo simulating, Monte Carlo EM algorithm has been successfully used to a wide range of applications. There is, however, the same shortage for EM algorithm and Monte Carlo EM algorithm, that the convergence rate of the two algorithms is linear. So this paper proposes the acceleration of Monte Carlo EM Algorithm, which is based on Monte Carlo EM Algorithm and Newton-Raphson algorithm, to improve the convergence rate. Thus the acceleration of Monte Carlo EM Algorithm has the advantages of both Monte Carlo EM Algorithm and Newton-Raphson algorithm, that is to say it facilitates E step by Monte Carlo simulation and also has quadratic convergence rate in a neighborhood of the posterior mode. Later its excellence in convergence rate is illustrated by a classical example.
Keywords: Augmentation data   Monte Carlo simulation   EM algorithm   Monte Carlo EM algorithm   Newton-Raphson algorithm.  
�ո����� 1900-01-01 �޻����� 1900-01-01 ����淢������  

ͨѶ����: �޼�


Copyright by Ӧ�ø���ͳ��