Efficient \(\mathrm{SMC}^2\) schemes for stochastic kinetic models
From MaRDI portal
Publication:2329744
DOI10.1007/s11222-017-9789-8zbMath1430.62054arXiv1704.02791OpenAlexW2607281807WikidataQ59612660 ScholiaQ59612660MaRDI QIDQ2329744
Andrew Golightly, Theodore Kypraios
Publication date: 18 October 2019
Published in: Statistics and Computing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1704.02791
Bayesian inferencesequential Monte Carlo (SMC)auxiliary particle filter (APF)Markov jump process (MJP)stochastic kinetic model (SKM)
Bayesian inference (62F15) Monte Carlo methods (65C05) Markov processes: hypothesis testing (62M02) Jump processes on discrete state spaces (60J74)
Related Items
Bayesian model discrimination for partially-observed epidemic models ⋮ Importance sampling for partially observed temporal epidemic models ⋮ Efficient sampling of conditioned Markov jump processes
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- The Correlated Pseudo-Marginal Method
- Sequential Bayesian inference in hidden Markov stochastic kinetic models with application to detection and response to seasonal epidemics
- On some properties of Markov chain Monte Carlo simulation methods based on the particle filter
- Particle learning and smoothing
- Stochastic epidemic models and their statistical analysis
- Simulation-based Bayesian inference for epidemic models
- On the efficiency of pseudo-marginal random walk Metropolis algorithms
- Bayesian inference for Markov jump processes with informative observations
- Efficient learning via simulation: a marginalized resample-move approach
- Twisted particle filters
- Bayesian inference for stochastic epidemic models with time-inhomogeneous removal rates
- A hierarchy of approximations of the master equation scaled by a size parameter
- Following a Moving Target—Monte Carlo Inference for Dynamic Bayesian Models
- Sequential Monte Carlo Methods in Practice
- Sequential Bayesian inference for implicit hidden Markov models and current limitations
- The Alive Particle Filter and Its Use in Particle Markov Chain Monte Carlo
- A sequential particle filter method for static models
- Filtering via Simulation: Auxiliary Particle Filters
- Particle Markov Chain Monte Carlo Methods
- Exact Filtering for Partially Observed Continuous Time Models
- Bayesian Inference for Stochastic Kinetic Models Using a Diffusion Approximation
- Alive SMC2: Bayesian model selection for low‐count time series models with intractable likelihoods
- SMC2: An Efficient Algorithm for Sequential Analysis of State Space Models
