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Locally scaled and stochastic volatility metropolis– hastings algorithms
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
6
Citations (Scopus)
Overview
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Mathematics
Metropolis-Hasting Algorithm
100%
Stochastic Volatility
100%
Effective Sample Size
66%
Monte Carlo
66%
Matrix (Mathematics)
33%
Markov Chain Monte Carlo
33%
Gaussian Distribution
33%
Predictive Performance
33%
Diffusion Process
33%
Random Walk
33%
Simulation Study
16%
Bayesian
16%
Monte Carlo Algorithm
16%
Posterior Distribution
16%
Hamiltonian Monte Carlo
16%
target distribution π
16%
Logistic Regression
16%
Autocorrelation
16%
Monte Carlo Technique
16%
Markov Chain Monte Carlo Method
16%
Real-World Data
16%
Sample Rate
16%
Proposal Distribution
16%
Closed Form
16%
Execution Time
16%
Local Geometry
16%
Computer Science
markov chain monte-carlo
100%
Predictive Performance
66%
Random Walk
66%
Diffusion Process
66%
Execution Time
33%
Logistic Regression
33%
Simulation Study
33%
Monte Carlo Technique
33%
Hamiltonian Monte Carlo
33%
Posterior Distribution
33%
Proposal Distribution
33%
Computational Time
33%
Target Distribution
33%
Keyphrases
Stochastic Volatility
100%
Effective Sample Size
36%
Langevin Monte Carlo
36%
No-U-Turn Sampler
27%
Jump-diffusion Process
18%
Autocorrelation
9%
Proposal Distribution
9%
Random Scaling
9%
Minor Addition
9%
Local Geometry
9%
Sampling Rate
9%
Vanilla
9%
Arts-based Methods
9%
Generated Samples
9%
Posterior Distribution
9%
Scaling Matrix
9%
Social Sciences
Stochastics
100%
Volatility
100%
Markov Chain Monte Carlo
50%