Does Waste Recycling Really Improve the Multi-Proposal Metropolis–Hastings algorithm? an Analysis Based on Control Variates [PDF]
Journal of Applied Probability, 2009 Jean-François Delmas, B. Jourdain
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Note on the Sampling Distribution for the Metropolis-Hastings Algorithm [PDF]
, 2003 John Geweke, Hisashi Tanizaki
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Elastic impedance inversion method based on Quantum Annealing MH algorithm [PDF]
E3S Web of Conferences, 2022 Seismic inversion is one of the important methods to realize reservoir prediction in oil and gas field exploration. The seismic stochastic inversion based on Metropolis-Hastings algorithm makes full use of well logging data and improves the vertical ...
Zhao Chen +6 more
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Convergence Rates for the Constrained Sampling via Langevin Monte Carlo
Entropy, 2023 Sampling from constrained distributions has posed significant challenges in terms of algorithmic design and non-asymptotic analysis, which are frequently encountered in statistical and machine-learning models.
Yuanzheng Zhu
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Locally Scaled and Stochastic Volatility Metropolis– Hastings Algorithms
Algorithms, 2021 Markov chain Monte Carlo (MCMC) techniques are usually used to infer model parameters when closed-form inference is not feasible, with one of the simplest MCMC methods being the random walk Metropolis–Hastings (MH) algorithm.
Wilson Tsakane Mongwe +2 more
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Accelerating equilibrium spin-glass simulations using quantum annealers via generative deep learning
SciPost Physics, 2023 Adiabatic quantum computers, such as the quantum annealers commercialized by D-Wave Systems Inc., are routinely used to tackle combinatorial optimization problems.
Giuseppe Scriva, Emanuele Costa, Benjamin McNaughton, Sebastiano Pilati
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DYNAMIC PROBABILITY SELECTION FOR FLOWER POLLINATION ALGORITHM BASED ON METROPOLISHASTINGS CRITERIA
Journal of ICT, 2020 Flower Pollination Algorithm (FPA) is a relatively new meta-heuristic algorithm that adopts its metaphor from the proliferation role of flowers in plants. Having only one parameter control (i.e.
Kamal Zuhairi Zamli +4 more
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The Metropolis-Hastings algorithm [PDF]
, 2015 This short note is a self-contained and basic introduction to the Metropolis-Hastings algorithm, this ubiquitous tool used for producing dependent simulations from an arbitrary distribution.
C. Robert
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Bayesian estimation of generalized partition of unity copulas
Dependence Modeling, 2020 This paper proposes a Bayesian estimation algorithm to estimate Generalized Partition of Unity Copulas (GPUC), a class of nonparametric copulas recently introduced by [18].
Masuhr Andreas, Trede Mark
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Majorize–Minimize Adapted Metropolis–Hastings Algorithm
IEEE Transactions on Signal Processing, 2020 The dimension and the complexity of inference problems have dramatically increased in statistical signal processing. It thus becomes mandatory to design improved proposal schemes in Metropolis-Hastings algorithms, providing large proposal transitions ...
Y. Marnissi +3 more
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