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GEOMETRIC ADAPTIVE MONTE CARLO IN RANDOM ENVIRONMENT
Theodore Papamarkou
, Alexey Lindo
,
Eric B. Ford
Astronomy & Astrophysics
Center for Astrostatistics
Center for Exoplanets & Habitable Worlds
Institute for Computational and Data Sciences (ICDS)
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Article
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peer-review
2
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Keyphrases
Computation Time
100%
Computational Cost
100%
Computational Complexity
100%
Local Geometry
100%
Geometric Information
100%
Random Environment
100%
Parameter Space
50%
Multiple Modes
50%
Strong Correlation
50%
Number of Steps
50%
Stationary Phase
50%
Frequent Use
50%
Fast Convergence Rate
50%
Markov Chain Monte Carlo Algorithm
50%
Transient Phase
50%
Effective Sample Size
50%
Discrete-time Stochastic Process
50%
Average-case Complexity
50%
Computer Science
Computational Complexity
100%
Computational Cost
100%
Geometric Information
100%
Computational Time
100%
markov chain monte-carlo
50%
discrete-time
50%
Fast Convergence
50%
Parameter Space
50%
Convergence Rate
50%
Engineering
Computational Time
100%
Computational Cost
100%
Computational Complexity
100%
Transients
50%
Discrete Time
50%
Parameter Space
50%
Stationary Phase
50%
Convergence Rate
50%
Mathematics
Monte Carlo
100%
Manifold
66%
Computational Cost
66%
Local Geometry
66%
Markov Chain Monte Carlo
33%
Discrete Time
33%
Parameter Space
33%
Convergence Rate
33%
Monte Carlo Algorithm
33%
Effective Sample Size
33%