A Markov chain Monte Carlo algorithm for Bayesian policy search
Systems Science & Control Engineering, 2018 Policy search algorithms have facilitated application of Reinforcement Learning (RL) to dynamic systems, such as control of robots. Many policy search algorithms are based on the policy gradient, and thus may suffer from slow convergence or local optima ...
Vahid Tavakol Aghaei +2 more
doaj +1 more source
Applied Sciences, 2019 Near-infrared spectroscopy (NIRS) including diffuse optical tomography is an imaging modality which makes use of diffuse light propagation in random media.
Yu Jiang +3 more
doaj +1 more source
Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models [PDF]
In this paper Efficient Importance Sampling (EIS) is used to perform a classical and Bayesian analysis of univariate and multivariate Stochastic Volatility (SV) models for financial return series. EIS provides a highly generic and very accurate procedure
Liesenfeld, Roman +1 more
core
Markov chain Monte Carlo algorithms with sequential proposals [PDF]
arXiv, 2019 We explore a general framework in Markov chain Monte Carlo (MCMC) sampling where sequential proposals are tried as a candidate for the next state of the Markov chain. This sequential-proposal framework can be applied to various existing MCMC methods, including Metropolis-Hastings algorithms using random proposals and methods that use deterministic ...
arxiv
Fleet‐Based Degradation State Quantification for Industrial Water Electrolyzers
Electrochemical Science Advances, EarlyView.ABSTRACT A reliable and continuous assessment of the degradation state of industrial water electrolyzers is crucial for maintenance planning and dispatch optimization, thus facilitating risk management for both suppliers and operators. Although voltage is a widely used and easily measurable degradation indicator, its effectiveness is compromised in ...
Xuqian Yan +3 more
wiley +1 more source
Automatic Parallel Tempering Markov Chain Monte Carlo with Nii-C
The Astrophysical Journal Supplement SeriesDue to the high dimensionality or multimodality that is common in modern astronomy, sampling Bayesian posteriors can be challenging. Several publicly available codes based on different sampling algorithms can solve these complex models, but the execution
Sheng Jin, Wenxin Jiang, Dong-Hong Wu
doaj +1 more source
Implementation of a practical Markov chain Monte Carlo sampling algorithm in PyBioNetFit. [PDF]
Bioinformatics, 2022 Neumann J +8 more
europepmc +1 more source
Convergence Bounds for Monte Carlo Markov Chains [PDF]
arXivThis review paper, written for the second edition of the Handbook of Markov Chain Monte Carlo, provides an introduction to the study of convergence analysis for Markov chain Monte Carlo (MCMC), aimed at researchers new to the field. We focus on methods for constructing bounds on the distance between the distribution of a Markov chain at a given time ...
arxiv
A multilevel Bayesian Markov Chain Monte Carlo Poisson modelling of factors associated with components of antenatal care offered to pregnant women in Nigeria. [PDF]
BMC Health Serv Res, 2023 Fagbamigbe OS +7 more
europepmc +1 more source
The application of Markov chain Monte Carlo methods to radiation hybrid mapping [PDF]
, 1997 Simon Heath
openalex +1 more source