Biased online parameter inference for state-space models
From MaRDI portal
Publication:1707039
DOI10.1007/S11009-016-9511-XzbMath1383.82052arXiv1503.00266OpenAlexW1579166438MaRDI QIDQ1707039
Pierre Del Moral, Ajay Jasra, Yan Zhou
Publication date: 28 March 2018
Published in: Methodology and Computing in Applied Probability (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1503.00266
Bayesian inference (62F15) Monte Carlo methods (65C05) Interacting random processes; statistical mechanics type models; percolation theory (60K35)
Related Items (4)
Sequential Bayesian inference for implicit hidden Markov models and current limitations ⋮ Uniform convergence over time of a nested particle filtering scheme for recursive parameter estimation in state-space Markov models ⋮ Unnamed Item ⋮ Stochastic Gradient MCMC for State Space Models
Cites Work
- Unnamed Item
- Unnamed Item
- The Correlated Pseudo-Marginal Method
- On particle methods for parameter estimation in state-space models
- On the convergence of adaptive sequential Monte Carlo methods
- Particle-kernel estimation of the filter density in state-space models
- A nonasymptotic theorem for unnormalized Feynman-Kac particle models
- Nested particle filters for online parameter estimation in discrete-time state-space Markov models
- Inference in hidden Markov models.
- Consistency of the maximum likelihood estimator for general hidden Markov models
- Following a Moving Target—Monte Carlo Inference for Dynamic Bayesian Models
- Theory of segmented particle filters
- Mean Field Simulation for Monte Carlo Integration
- Sequential Monte Carlo Samplers
- A Monte Carlo Approach to Filtering for a Class of Marked Doubly Stochastic Poisson Processes
- Practical Filtering with Sequential Parameter Learning
- Particle Markov Chain Monte Carlo Methods
- The Bernstein-Von Mises Theorem for Markov Processes
- SMC2: An Efficient Algorithm for Sequential Analysis of State Space Models
- Path storage in the particle filter
This page was built for publication: Biased online parameter inference for state-space models
