Abstract
The authors thank the Isaac Newton Institute for Mathematical Sciences for support and hospitality during the programme Monte Carlo Inference for Complex Statistical Models when work on this paper was undertaken. This work was supported by the Engineering and Physical Sciences Research Council [grant numbers EP/K020153/1, EP/K032208/1] and the Swedish Research Council [contract number 2016-04278].
Highlights
mimics sampling from φT
constant depending on the mixing coecients of the model
The blocked Particle Gibbs sampler PN can be seen as a perturbation of the ideal blocked Gibbs sampler P
Summary
F. Lindsten, R. Douc, and E. Moulines, Uniform ergodicity of the Particle Gibbs sampler. Scandinavian Journal of Statistics, 42(3): 775-797, 2015. S. S. Singh, F. Lindsten, and E. Moulines, Blocking Strategies and Stability of Particle Gibbs Samplers. arXiv:1509.08362, 2015. Consider a nonlinear discrete-time state-space model, and X1 ∼ μ. Xt | Xt−1 ∼ mθ(Xt−1, ·), Yt | Xt ∼ gθ(Xt, ·), We observe Y1:T = (y1, . . . , yT ) and wish to estimate θ and/or X1:T .
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