Exact and approximate Bayesian inference for low integer-valued time series models with intractable likelihoods

DOI10.1214/15-BA950zbMath1359.62365OpenAlexW2060016742WikidataQ62899365 ScholiaQ62899365MaRDI QIDQ516464

Anthony N. Pettitt, Roy A. McCutchan, Christopher C. Drovandi

Publication date: 14 March 2017

Published in: Bayesian Analysis (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ba/1429543852

zbMATH Keywords

Markov processbranching processparticle filterapproximate Bayesian computationparticle Markov chain Monte CarloINARMA modelpseudo-marginal methods

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Bayesian inference (62F15) Monte Carlo methods (65C05) Branching processes (Galton-Watson, birth-and-death, etc.) (60J80)

Related Items (9)

Approximate Bayesian computation in controlled branching processes: the role of summary statistics ⋮ Efficient Bayesian model choice for partially observed processes: with application to an experimental transmission study of an infectious disease ⋮ Approximate Bayesian computation and simulation-based inference for complex stochastic epidemic models ⋮ Exact and approximate Bayesian inference for low integer-valued time series models with intractable likelihoods ⋮ Alive SMC²: Bayesian model selection for low‐count time series models with intractable likelihoods ⋮ Spectral density-based and measure-preserving ABC for partially observed diffusion processes. An illustration on Hamiltonian SDEs ⋮ On MCMC sampling in self-exciting integer-valued threshold time series models ⋮ Approximate Bayesian computation approach on the maximal offspring and parameters in controlled branching processes ⋮ Efficient sampling of conditioned Markov jump processes

Uses Software

• Expokit
• CODA

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