Cargando…
Efficient inference in state-space models through adaptive learning in online Monte Carlo expectation maximization
Expectation maximization (EM) is a technique for estimating maximum-likelihood parameters of a latent variable model given observed data by alternating between taking expectations of sufficient statistics, and maximizing the expected log likelihood. For situations where sufficient statistics are int...
Autores principales: | Henderson, Donna, Lunter, Gerton |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7382664/ https://www.ncbi.nlm.nih.gov/pubmed/32764847 http://dx.doi.org/10.1007/s00180-019-00937-4 |
Ejemplares similares
-
Monte Carlo samplers for efficient network inference
por: Kilic, Zeliha, et al.
Publicado: (2023) -
A general algorithm for error-in-variables regression modelling using Monte Carlo expectation maximization
por: Stoklosa, Jakub, et al.
Publicado: (2023) -
Demographic inference from multiple whole genomes using a particle filter for continuous Markov jump processes
por: Henderson, Donna, et al.
Publicado: (2021) -
Online Bayesian Phylogenetic Inference: Theoretical Foundations via Sequential Monte Carlo
por: Dinh, Vu, et al.
Publicado: (2018) -
Monte Carlo phase space
por: James, Frederick E
Publicado: (1968)