EM algorithmRobustnessThe Heckman selection model is widely used to analyse data for which the outcome is partially observable, and the missing part is not random. The 2-step method, maximum likelihood estimation (MLE), and EM algorithms have been developed to analyse this model; however, they ...
A model‐based approach was therefore proposed, and due to the discrete nature of the data, a truncated mixture of bivariate beta distributions was fitted to the data using an expectation–maximization algorithm. However, unlike the usual approach for mixture density estimation problems, to each 76...
SEAM: a Stochastic EM-type Algorithm for Motif-finding in biopolymer sequences. Position weight matrix-based statistical modeling for the identification and characterization of motif sites in a set of unaligned biopolymer sequences is ... BI Chengpeng - 《Journal of Bioinformatics & Computational Bio...
Telek. A Novel Approach for Phase-Type Fitting with the EM Algorithm. IEEE Transactions on Dependable and Secure Computing, 3(3):245-258, 2006.Thummler, A., Buchholz, P., & Telek, M. (2006). A novel approach for phase-type fitting with the EM algorithm. IEEE Transactions on ...
weights are cell type proportions. A standard EM algorithm can be used to estimate the parameters of this mixture of regression model. From our exploratory analysis, we found the estimation may be unstable when two or more cell types have highly correlated cell type-specific DNA methylation. To...
Energy minimization was carried out until convergence using a steepest descents algorithm for 5000 steps. The system was then equilibrated in 5 cycles, gradually reducing constraints, and increasing time-step, as suggested by the CHARMM-GUI server, using the leap-frog algorithm, Verlet cut-off ...
The EM algorithm concerns maximum likelihood estimation for incomplete data. We present a simulated annealing type EM algorithm: SAEM. This algorithm is an adaptation of the stochastic EM algorithm (SEM) that we have previously developed. Like SEM, SAEM overcomes most of the well-known limitations...
Meanwhile, aniterative channel estimationalgorithm, which can improve bit error rate (BER) performance of proposed Turbo coded GMSK signal transmission scheme dramatically, was expressed. 同时,针对所设计的Turbo编码和GMSK调制相结合的信号传输体制,提出了迭代信道估计算法,明显改善了系统的误码率BER性能。
An EM-type algorithm for multivariate mixture models This paper introduces a new approach, based on dependent univariate GLMs, for fitting multivariate mixture models. This approach is a multivariate generali... GR Oskrochi,RB Davies - 《Statistics & Computing》 被引量: 10发表: 1997年 ...
2018年7月11日EM算法也称期望最大化(Expectation-Maximum,简称EM)算法,它是一个基础算法,是很多机器学习领域算法的基础,比如隐式马尔科夫算法(HMM), LDA ... https://zhuanlan.zhihu.com/p/36331115 收藏 赞 em算法实例讲解,值得推荐 - zhwa - 博客园 2019年12月17日转自https://blog.csdn.net/v_JULY_v...