Approximate Inference for Observation-Driven Time Series Models with Intractable Likelihoods

DOI10.1145/2592254zbMath1322.65011arXiv1303.7318OpenAlexW2005630497MaRDI QIDQ5176484

Nikolas Kantas, Elena Ehrlich, Ajay Jasra

Publication date: 26 February 2015

Published in: ACM Transactions on Modeling and Computer Simulation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1303.7318

zbMATH Keywords

Markov chain Monte Carloasymptotic consistencyapproximate Bayesian computationobservation-driven time series models

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Bayesian inference (62F15) Probabilistic models, generic numerical methods in probability and statistics (65C20)

Related Items (6)

Bayesian inference for fractional oscillating Brownian motion ⋮ An approximate likelihood perspective on ABC methods ⋮ Approximate Bayesian Computation for a Class of Time Series Models ⋮ Approximate Bayesian Computation: A Survey on Recent Results ⋮ A simple approach to maximum intractable likelihood estimation ⋮ Multilevel Monte Carlo in approximate Bayesian computation

Cites Work

• Unnamed Item
• An adaptive sequential Monte Carlo method for approximate Bayesian computation
• Approximate Bayesian computational methods
• Filtering via approximate Bayesian computation
• Nonlinear time series. Nonparametric and parametric methods
• Convergence properties of pseudo-marginal Markov chain Monte Carlo algorithms
• Ergodicity of observation-driven time series models and consistency of the maximum likelihood estimator
• On mixtures of \(\chi^ 2\)-and \(F\)-distributions which yield distributions of the same family
• Parameter Estimation for Hidden Markov Models with Intractable Likelihoods
• The Alive Particle Filter and Its Use in Particle Markov Chain Monte Carlo
• Adaptive approximate Bayesian computation
• A Method for Simulating Stable Random Variables
• Expectation Propagation for Likelihood-Free Inference
• Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator

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