What is recorded here?
Particle filters approximate hidden states using a finite collection of simulated possibilities. MCMC methods instead build a chain designed to preserve a target distribution. Particle MCMC connects the two by treating the random choices inside a particle filter as part of a larger mathematical state. This allows particle-based proposals to preserve the intended posterior exactly under stated assumptions, even with finitely many particles. Exactness of the stationary target still leaves a practical question: how quickly does the chain explore it?
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The three-author 2010 framework, including particle independent Metropolis-Hastings, particle marginal Metropolis-Hastings and particle Gibbs. Target invariance and convergence require their separate support, resampling and ideal-chain assumptions. Particle-Gibbs invariance at N>=1 is not ergodicity at N=1; the convergence theorem requires N>=2. Existing SMC and pseudo-marginal results retain their own credit; no universal fast-mixing guarantee.
Read the original work
- Wiley full text for particle Markov chain Monte Carlo methods
- Oxford author-hosted particle MCMC manuscript
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
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People and systems
Christophe Andrieu; Arnaud Doucet; Roman Holenstein
Source-listed authors for the exact contribution; experimental subjects are not autonomous scientific authors. Source support and predecessor work retain separate roles. Individual effort shares and unresolved identities are not inferred.
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English catalogue narrative
Andrieu, Doucet and Holenstein: particle Markov chain Monte Carlo methods
Particle filters approximate hidden states using a finite collection of simulated possibilities. MCMC methods instead build a chain designed to preserve a target distribution. Particle MCMC connects the two by treating the random choices inside a particle filter as part of a larger mathematical state. This allows particle-based proposals to preserve the intended posterior exactly under stated assumptions, even with finitely many particles. Exactness of the stationary target still leaves a practical question: how quickly does the chain explore it?
Scientific scope
The three-author 2010 framework, including particle independent Metropolis-Hastings, particle marginal Metropolis-Hastings and particle Gibbs. Target invariance and convergence require their separate support, resampling and ideal-chain assumptions. Particle-Gibbs invariance at N>=1 is not ergodicity at N=1; the convergence theorem requires N>=2. Existing SMC and pseudo-marginal results retain their own credit; no universal fast-mixing guarantee.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Christophe Andrieu; Arnaud Doucet; Roman Holenstein
Source-listed authors for the exact contribution; experimental subjects are not autonomous scientific authors. Source support and predecessor work retain separate roles. Individual effort shares and unresolved identities are not inferred.