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torchtree: Flexible Phylogenetic Model Development and Inference Using PyTorch. [PDF]
Syst BiolFourment M +5 more
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Statistics and Computing, 2008
We review adaptive Markov chain Monte Carlo algorithms (MCMC) as a mean to optimise their performance. Using simple toy examples we review their theoretical underpinnings, and in particular show why adaptive MCMC algorithms might fail when some fundamental properties are not satisfied.
Christophe Andrieu, Andrieu Christophe
exaly +4 more sources
We review adaptive Markov chain Monte Carlo algorithms (MCMC) as a mean to optimise their performance. Using simple toy examples we review their theoretical underpinnings, and in particular show why adaptive MCMC algorithms might fail when some fundamental properties are not satisfied.
Christophe Andrieu, Andrieu Christophe
exaly +4 more sources
MCMC and the fibonacci distribution
Communications in Statistics - Simulation and Computation, 2015ABSTRACTA method to create a Markov transition matrix for Markov chain Monte Carlo studies is presented and applied to the Fibonacci probability distribution.
openaire +1 more source
Statistics and Computing, 2006
We propose to combine two quite powerful ideas that have recently appeared in the Markov chain Monte Carlo literature: adaptive Metropolis samplers and delayed rejection. The ergodicity of the resulting non-Markovian sampler is proved, and the efficiency of the combination is demonstrated with various examples.
H. HAARIO +3 more
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We propose to combine two quite powerful ideas that have recently appeared in the Markov chain Monte Carlo literature: adaptive Metropolis samplers and delayed rejection. The ergodicity of the resulting non-Markovian sampler is proved, and the efficiency of the combination is demonstrated with various examples.
H. HAARIO +3 more
openaire +3 more sources
SPE EUROPEC/EAGE Annual Conference and Exhibition, 2010
Abstract The ensemble Kalman filter (EnKF) has recently become a popular history-matching tool largely because of its computational efficiency and ease of implementation. While EnKF has improved a previous history match obtained manually in several field cases, and often appears to give reasonable results for realistic synthetic history ...
Alexandre A. Emerick, Albert C. Reynolds
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Abstract The ensemble Kalman filter (EnKF) has recently become a popular history-matching tool largely because of its computational efficiency and ease of implementation. While EnKF has improved a previous history match obtained manually in several field cases, and often appears to give reasonable results for realistic synthetic history ...
Alexandre A. Emerick, Albert C. Reynolds
openaire +1 more source
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
2003
Markov chain Monte Carlo (MCMC) algorithms are now widely used in virtually all areas of statistics. In particular, spatial applications featured very prominently in the early development of the methodology (Geman & Geman 1984), and they still provide some of the most challenging problems for MCMC techniques.
Petros Dellaportas, Gareth O. Roberts
openaire +1 more source
Markov chain Monte Carlo (MCMC) algorithms are now widely used in virtually all areas of statistics. In particular, spatial applications featured very prominently in the early development of the methodology (Geman & Geman 1984), and they still provide some of the most challenging problems for MCMC techniques.
Petros Dellaportas, Gareth O. Roberts
openaire +1 more source