Exact and computationally efficient Bayesian inference for generalized Markov modulated Poisson processes

DOI10.1007/s11222-021-10074-yzbMath1482.62012arXiv2006.09949OpenAlexW3036882973MaRDI QIDQ2114041

Lívia M. Dutra, Roger W. C. Silva, Flávio B. Gonçalves

Publication date: 14 March 2022

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2006.09949

zbMATH Keywords

uniformizationMetropolis-Hastings algorithmfast computation

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08)

Uses Software

• Ox
• covid19br

Cites Work

• Unnamed Item
• Bayesian inference for Hawkes processes
• Simulation from endpoint-conditioned, continuous-time Markov chains on a finite state space, with applications to molecular evolution
• Optimal scaling of the independence sampler: theory and practice
• Rates of convergence of the Hastings and Metropolis algorithms
• An Exact Gibbs Sampler for the Markov-Modulated Poisson Process
• A Kernel Method for Smoothing Point Process Data
• Log Gaussian Cox Processes
• Exact Bayesian Inference in Spatiotemporal Cox Processes Driven by Multivariate Gaussian Processes
• Bayesian Inference for Matérn Repulsive Processes

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