Results 241 to 250 of about 168,901 (276)
Some of the next articles are maybe not open access.
2014
AbstractThis chapter provides a detailed introduction to modern Bayesian computation. The Metropolis–Hastings algorithm is illustrated using a simple example of distance estimation between two sequences. A number of generic Markov chain Monte Carlo (MCMC) proposal moves are described, and the calculation of their proposal ratios is illustrated.
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AbstractThis chapter provides a detailed introduction to modern Bayesian computation. The Metropolis–Hastings algorithm is illustrated using a simple example of distance estimation between two sequences. A number of generic Markov chain Monte Carlo (MCMC) proposal moves are described, and the calculation of their proposal ratios is illustrated.
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2004
We maintain that the analysis and synthesis of random fields is much faster in a hierarchical setting. In particular, complicated long-range interactions at a fine scale become progressively more local (and therefore more efficient) at coarser levels. The key to effective coarse-scale activity is the proper model definition at those scales. This can be
openaire +1 more source
We maintain that the analysis and synthesis of random fields is much faster in a hierarchical setting. In particular, complicated long-range interactions at a fine scale become progressively more local (and therefore more efficient) at coarser levels. The key to effective coarse-scale activity is the proper model definition at those scales. This can be
openaire +1 more source
On MCMC sampling in self-exciting integer-valued threshold time series models
Computational Statistics and Data Analysis, 2022Kai Yang, Xiaogang Dong
exaly
MCMC algorithms for Subset Simulation
Probabilistic Engineering Mechanics, 2015Iason Papaioannou +2 more
exaly
Bayesian MCMC flood frequency analysis with historical information
Journal of Hydrology, 2005Dirceu Reis
exaly