Comments (6)
See second section of this tutorial from the documentation:
https://particles-sequential-monte-carlo-in-python.readthedocs.io/en/latest/notebooks/Bayes_estimation_ssm.html
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Thank you, I saw particle marginal metropolis Hastings, I can't see a method for particle metropolis Hastings
from particles.
What do you mean by "Particle MH"? In the Holenstein et al paper, they talk about PIMH="particle independent Metropolis-Hastings", PMMH = P marginal MH", and Particle Gibbs ? Maybe you mean PIMH ?
from particles.
Particle Metropolis Hastings: https://arxiv.org/pdf/1511.01707.pdf
from particles.
What they call PMH in that paper is PMMH, it's the same algorithm.
from particles.
Ah ok thank you very much, sorry for this
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Related Issues (20)
- Error in guided particle filtering for pre-implemented stochastic volatility model. HOT 4
- Restricting sampling of parameters HOT 4
- Information request regarding particle attribute and predictive steps HOT 1
- nsteps in backward_sampling_mcmc
- Categorical distribution
- Automate upper_bound_log_pt
- Mixture error? HOT 3
- Potential Bug in SMC2 HOT 1
- Forum Creation HOT 3
- Not working given example in the Kalman file HOT 1
- Bug in BayesianVS_gprior HOT 1
- Feature request: parallelisation over the particles? HOT 3
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- Problem with missing data HOT 6
- Sensor fusion - multiple `PY` depending on which sensor is returning HOT 6
- Is there a fast way to define a vector of parameters? HOT 17
- Question : Is it possible to use control commands `u` in `PX`? HOT 2
- How to get the distribution of x in the lib? HOT 1
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