Abstract
This article shows how the EM algorithm can be sped up by computing the gradient and Hessian of the observed log-likelihood and then using a Newton algorithm. The gradient and Hessian are computed using two identities associated with the expected log-likelihood. Illustrative examples are provided for co-variance estimation with missing observations and factor analysis.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
