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stochastic approximation of expectation-maximization (saem) algorithm  (MathWorks Inc)


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    MathWorks Inc stochastic approximation of expectation-maximization (saem) algorithm
    Parameter estimates of the metastatic and survival models obtained by likelihood maximization via the <t> SAEM </t> algorithm.
    Stochastic Approximation Of Expectation Maximization (Saem) Algorithm, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/stochastic+approximation+expectation+maximization+algorithm/pmc11095706-111-14-21
    Average 90 stars, based on 1 article reviews
    stochastic approximation of expectation-maximization (saem) algorithm - by Bioz Stars, 2026-09
    90/100 stars

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    1) Product Images from "Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment"

    Article Title: Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment

    Journal: PLOS Computational Biology

    doi: 10.1371/journal.pcbi.1012088

    Parameter estimates of the metastatic and survival models obtained by likelihood maximization via the SAEM algorithm.
    Figure Legend Snippet: Parameter estimates of the metastatic and survival models obtained by likelihood maximization via the SAEM algorithm.

    Techniques Used: In Vitro

    Related Articles

    other:

    Article Title: Formation of parametric images using mixed-effects models: a feasibility study.
    Article Snippet: Mixed-effects models have been widely used in the analysis of longitudinal data.. By presenting the parameters as a combination of fixed effects and random effects, mixed-effects models incorporating both withinand betweensubject variations are capable of improving parameter estimation.. In this work, we demonstrate the feasibility of using a non-linear mixed-effects (NLME) approach for generating parametric images from medical imaging data of a single study.



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    Image Search Results


    Parameter estimates of the metastatic and survival models obtained by likelihood maximization via the SAEM algorithm.

    Journal: PLOS Computational Biology

    Article Title: Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment

    doi: 10.1371/journal.pcbi.1012088

    Figure Lengend Snippet: Parameter estimates of the metastatic and survival models obtained by likelihood maximization via the SAEM algorithm.

    Article Snippet: Maximum likelihood estimates of the population parameters were obtained using the Stochastic Approximation of Expectation-Maximization (SAEM) algorithm implemented in the nlmefitsa Matlab function [ ].

    Techniques: In Vitro