Efficient inference in state-space models through adaptive learning in online Monte Carlo expectation maximization

DOI10.1007/s00180-019-00937-4zbMath1505.62185arXiv1807.03265OpenAlexW2992026747WikidataQ98238517 ScholiaQ98238517MaRDI QIDQ2203422

Donna Henderson, Gerton Lunter

Publication date: 6 October 2020

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1807.03265

zbMATH Keywords

sequential Monte Carloonline estimationstochastic approximation expectation maximizationlatent variable model

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Monte Carlo methods (65C05) Stochastic approximation (62L20)

Uses Software

• Adam
• GitHub
• ADADELTA

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• AN APPROACH TO TIME SERIES SMOOTHING AND FORECASTING USING THE EM ALGORITHM
• Lookahead strategies for sequential Monte Carlo
• Sequential Monte Carlo smoothing with application to parameter estimation in nonlinear state space models
• Convergence of a stochastic approximation version of the EM algorithm
• Online expectation maximization based algorithms for inference in hidden Markov models
• Sequential Monte Carlo Methods in Practice
• On-Line Expectation–Maximization Algorithm for latent Data Models
• Particle filters and Bayesian inference in financial econometrics
• Online Learning with Hidden Markov Models
• Simple and Globally Convergent Methods for Accelerating the Convergence of Any EM Algorithm
• A stochastic approximation type EM algorithm for the mixture problem
• Filtering via Simulation: Auxiliary Particle Filters
• Conjugate Gradient Acceleration of the EM Algorithm
• Bayesian Analysis of Isochores