Sampling Latent States for High-Dimensional Non-Linear State Space Models with the Embedded HMM Method
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[1] M. Pitt,et al. Filtering via Simulation: Auxiliary Particle Filters , 1999 .
[2] Radford M. Neal. Markov Chain Sampling for Non-linear State Space Models Using Embedded Hidden Markov Models , 2003, math/0305039.
[3] Radford M. Neal,et al. Ecient Bayesian inference for stochastic volatility models with ensemble MCMC methods , 2014, 1412.3013.
[4] A. Doucet,et al. Particle Markov chain Monte Carlo methods , 2010 .
[5] A. P. Dawid,et al. Regression and Classification Using Gaussian Process Priors , 2009 .
[6] Radford M. Neal,et al. MCMC for non-Linear State Space Models Using Ensembles of Latent Sequences , 2013 .
[7] Radford M. Neal,et al. Inferring State Sequences for Non-linear Systems with Embedded Hidden Markov Models , 2003, NIPS.
[8] Fredrik Lindsten,et al. On the use of backward simulation in the particle Gibbs sampler , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[9] Alexander Y. Shestopaloff. MCMC methods for non-linear state space models , 2016 .