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Antithetic magnetic and shadow Hamiltonian Monte Carlo
Wilson Tsakane Mongwe
, Rendani Mbuvha
,
Tshilidzi Marwala
Faculty of Engineering and the Built Environment
University of Johannesburg
University of the Witwatersrand
Research output
:
Contribution to journal
›
Article
›
peer-review
16
Citations (Scopus)
Overview
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Mathematics
Hamiltonian Monte Carlo
100%
Monte Carlo Algorithm
45%
Hamiltonian
36%
Monte Carlo
36%
Effective Sample Size
27%
Bayesian
18%
Markov Chain Monte Carlo
18%
Variance
18%
Logistic Regression
9%
Autocorrelation
9%
Markov Chain Monte Carlo Method
9%
Higher Dimensions
9%
Importance Sampling
9%
Network Model
9%
Neural Network
9%
Sampling Technique
9%
Execution Time
9%
Keyphrases
Magnetic Hamiltonian
100%
Antithetic Sampling
66%
Monte Carlo Estimator
33%
Inference Problem
16%
Importance Sampling
16%
Classical Monte Carlo Simulations
16%
Importance Sampler
16%
Posterior Inference
16%
Psychology
Markov Chain Monte Carlo
100%
Neural Network
50%
Network Model
50%
Monte Carlo Markov Chain Method
50%
Computer Science
Hamiltonian Monte Carlo
100%
markov chain monte-carlo
27%
Monte Carlo Estimator
18%
Machine Learning
9%
Neural Network Model
9%
Execution Time
9%
Logistic Regression
9%
Sampling Technique
9%
Importance Sampling
9%
Importance Sampler
9%
Chemistry
Hybrid Monte Carlo
100%
Monte Carlo Method
66%
Arts and Humanities
Magnetic
100%
Literature
14%
Usefulness
14%
Execution
14%
Technique
14%