Efficient Parameter Sampling for Markov Jump Processes

DOI10.1080/10618600.2020.1763807OpenAlexW3020823106MaRDI QIDQ5066413

Boqian Zhang, Unnamed Author

Publication date: 29 March 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1704.02369

zbMATH Keywords

Markov chain Monte Carlouniformizationgeometric ergodicitycontinuous-time Markov chainMetropolis-Hastings

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Pseudo-Marginal Inference for CTMCs on Infinite Spaces via Monotonic Likelihood Approximations

Uses Software

• CODA
• BayesDA

Cites Work

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• The pseudo-marginal approach for efficient Monte Carlo computations
• A general framework for the parametrization of hierarchical models
• Eigenvalue bounds on convergence to stationarity for nonreversible Markov chains, with an application to the exclusion process
• Option pricing for pure jump processes with Markov switching compensators
• Simulation from endpoint-conditioned, continuous-time Markov chains on a finite state space, with applications to molecular evolution
• Geometric ergodicity of Rao and Teh's algorithm for Markov jump processes and CTBNs
• Intrusion Detection using Continuous Time Bayesian Networks
• An Exact Gibbs Sampler for the Markov-Modulated Poisson Process
• Markov Chains and Stochastic Stability
• Markov chain Monte Carlo in conditionally Gaussian state space models
• DATA AUGMENTATION AND DYNAMIC LINEAR MODELS
• Particle Markov Chain Monte Carlo Methods
• An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants

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