Likelihood-free stochastic approximation EM for inference in complex models

DOI10.1080/03610918.2017.1401082OpenAlexW2531549810MaRDI QIDQ5086194

Umberto Picchini

Publication date: 1 July 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1609.03508

zbMATH Keywords

incomplete datastate-space modelintractable likelihoodSAEMsynthetic likelihood

Mathematics Subject Classification ID

Non-Markovian processes: estimation (62M09) Point estimation (62F10) Signal detection and filtering (aspects of stochastic processes) (60G35)

Cites Work

• Unnamed Item
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• Bayesian Synthetic Likelihood
• The pseudo-marginal approach for efficient Monte Carlo computations
• On particle methods for parameter estimation in state-space models
• Estimation in the partially observed stochastic Morris-Lecar neuronal model with particle filter and stochastic approximation methods
• Approximate Bayesian computational methods
• Filtering via approximate Bayesian computation
• Computing a nearest symmetric positive semidefinite matrix
• Coupling stochastic EM and approximate Bayesian computation for parameter inference in state-space models
• Convergence of a stochastic approximation version of the EM algorithm
• On the efficiency of pseudo-marginal random walk Metropolis algorithms
• Inference in hidden Markov models.
• Monte Carlo strategies in scientific computing.
• Sequential Monte Carlo Methods in Practice
• Inference for dynamic and latent variable models via iterated, perturbed Bayes maps
• Particle Markov Chain Monte Carlo Methods
• Constructing Summary Statistics for Approximate Bayesian Computation: Semi-Automatic Approximate Bayesian Computation
• An adaptive Metropolis algorithm