Added a new class ParallelTempering to the inference.mcmc module which runs the 'parallel tempering' MCMC algorithm. ParallelTempering uses Python's multiprocessing module to create separate, dedicated processes for each Markov-chain object involved in the parallel tempering algorithm, allowing the necessary computations for each object to take place in parallel if multiple CPU threads are available
The Markov Chain Monte Carlo (MCMC) method is a statistical almost experimental approach to computin...
Embarrassingly parallel Markov Chain Monte Carlo (MCMC) exploits parallel computing to scale Bayesia...
It is well known that traditional Markov chain Monte Carlo (MCMC) methods can fail to effectively ex...
Abstract: Developing efficient MCMC algorithms is indispensable in Bayesian inference. In parallel t...
The increasing availability of multi-core and multiprocessor architectures provides new opportunitie...
Accelerating Markov chain Monte Carlo via parallel predictive prefetching We present a general frame...
In recent years, parallel processing has become widely available to researchers. It can be applied i...
Accelerating Markov chain Monte Carlo via parallel predictive prefetching We present a general frame...
Parallel tempering is a generic Markov chainMonteCarlo samplingmethod which allows good mixing with ...
Parallel predictive prefetching is a new frame-work for accelerating a large class of widely-used Ma...
<p>Communication costs, resulting from synchronization requirements during learning, can greatly slo...
There exist a large number of computationally intensive statistical procedures that can be implement...
One of the major concerns for Markov Chain Monte Carlo (MCMC) algorithms is that they can take a lon...
Communication costs, resulting from synchro-nization requirements during learning, can greatly slow ...
Markov chain Monte Carlo methods are a powerful and commonly used family of numerical methods for sa...
The Markov Chain Monte Carlo (MCMC) method is a statistical almost experimental approach to computin...
Embarrassingly parallel Markov Chain Monte Carlo (MCMC) exploits parallel computing to scale Bayesia...
It is well known that traditional Markov chain Monte Carlo (MCMC) methods can fail to effectively ex...
Abstract: Developing efficient MCMC algorithms is indispensable in Bayesian inference. In parallel t...
The increasing availability of multi-core and multiprocessor architectures provides new opportunitie...
Accelerating Markov chain Monte Carlo via parallel predictive prefetching We present a general frame...
In recent years, parallel processing has become widely available to researchers. It can be applied i...
Accelerating Markov chain Monte Carlo via parallel predictive prefetching We present a general frame...
Parallel tempering is a generic Markov chainMonteCarlo samplingmethod which allows good mixing with ...
Parallel predictive prefetching is a new frame-work for accelerating a large class of widely-used Ma...
<p>Communication costs, resulting from synchronization requirements during learning, can greatly slo...
There exist a large number of computationally intensive statistical procedures that can be implement...
One of the major concerns for Markov Chain Monte Carlo (MCMC) algorithms is that they can take a lon...
Communication costs, resulting from synchro-nization requirements during learning, can greatly slow ...
Markov chain Monte Carlo methods are a powerful and commonly used family of numerical methods for sa...
The Markov Chain Monte Carlo (MCMC) method is a statistical almost experimental approach to computin...
Embarrassingly parallel Markov Chain Monte Carlo (MCMC) exploits parallel computing to scale Bayesia...
It is well known that traditional Markov chain Monte Carlo (MCMC) methods can fail to effectively ex...