Methods: The mathematical derivation of a Negative Binomial Maximum Likelihood Expectation-Maximization (NB-MLEM) algorithm is presented. A simulation study to compare the performance of the proposed NB-MLEM al
In this study, we propose a super-spatial resolution method combined with the maximum-likelihood expectation maximization (MLEM) algorithm to obtain a high-resolution image from the alpha imaging detector. Fig. 1 shows a description of the measurement technique for the super-spatial resolution method...
Maximum likelihood expectation maximization (MLEM) MLEM approach involves maximizing the log likelihood function of Poisson statistics. MLEM can provide accurate quantitative reconstructions compared to analytical techniques in the limited data situations. The formulation for MLEM type approach can be written ...
In the Expectation step, find the expected values of the latent variables (here you need to use the current parameter values) • In the Maximization step, first plug in the expected values of the latent variables in the log-likelihood of the ...
The expectation-maximization (EM) algorithm for maximum likelihood estimation can be applied to image reconstruction in positron emission tomography (PET) and time-of-flight assisted PET (TOFPET). To implement this algorithm, one can employ either the projection data acquired at various angles or ...
1. 首先是 E 步骤求期望 (expectation),即计算在第k次迭代模型参数为\lambda^{(k)}时潜在变量的后验概率分布P(\psi|\boldsymbol O,\lambda^{(k)}),这通过前后向算法计算可得,然后计算这个后验概率分布对于新的模型参数\lambda^{(k+1)}在所有的训练数据下对数似然函数的期望 (expected log-likelihood of...
the Maximization step, where we maximize the expectation of the complete-datalog-likelihood, computed with respect to the conditional probabilities found in the Expectation step. The result of the maximization is a new parameter vector . The iterations end when a stopping criterion is met (e.g.,...
A wobbling and line spread function (LSF)-based maximum likelihood expectation maximization (WL-MLEM) algorithm, which combined the MLEM iterative reconstruction algorithm with wobbled sampling and LSF-based deconvolution using the system matrix, was proposed for improving the spatial resolution of PET ...
Wu BL, Liu NJ2 and Zhao HY (2006) PSMIX: an R package for population structure inference via maximum likelihood method BMC Bioinformatics, 7:317Wu B, Nianjun L, Hongyu Z (2006) PSMIX: an R package for population structure inference via maximum likelihood method. BMC Bioinformatics 7: 317...
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