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1 parentf20e4f8 commit2eb04d0Copy full SHA for 2eb04d0
README.Rmd
@@ -56,11 +56,11 @@ the marginal likelihood is intractable. Thus, standard MCMC methods like
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Hamiltonian Monte Carlo (HMC) or Metropolis-Hastings (MH) cannot be applied
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directly.
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-TheParticle Markov Chain Monte Carlo (PMCMC) methods, such as the Particle
+Particle Markov Chain Monte Carlo (PMCMC) methods, such as the Particle
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Marginal Metropolis-Hastings (PMMH) implemented in this package, are designed
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to handle these situations. They use particle filters to approximate the
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-marginal likelihood and allow for efficient sampling from the posterior
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-density of the latent states and parameters.
+marginal likelihood and allow for efficient sampling from thejointposterior
+density.
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##State-space Models
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README.md
@@ -46,11 +46,11 @@ and the marginal likelihood is intractable. Thus, standard MCMC methods
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like Hamiltonian Monte Carlo (HMC) or Metropolis-Hastings (MH) cannot be
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applied directly.
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-TheParticle Markov Chain Monte Carlo (PMCMC) methods, such as the
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-ParticleMarginal Metropolis-Hastings (PMMH) implemented in this
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-package, aredesigned to handle these situations. They use particle
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-filters toapproximate the marginal likelihood and allow for efficient
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-samplingfrom the posterior density of the latent states and parameters.
+Marginal Metropolis-Hastings (PMMH) implemented in this package, are
+designed to handle these situations. They use particle filters to
+approximate the marginal likelihood and allow for efficient sampling
+from thejointposterior density.
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