Equation of State Calculations by Fast Computing Machines

By Vivek S. Borkar. 1953

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Citations in the corpus

Plug and Play Language Models: A Simple Approach to Controlled Text Generation (2019, methodology): “Sophisticated sampling methods (Metropolis et al., 1953) can be used to constrain the model generation to certain keywords and topics.”
A deterministic and computable Bernstein-von Mises theorem (2019, methodology): “Sampling methods, dating back to the famed Metropolis-Hastings algorithm (Metropolis et al. [1953], Hastings [1970]), provide a first possible solution: approximate the posterior using a large number of samples whose marginal distribution is the posterior distribution (Brooks et al. [2011]). Any exp”
An Explanation of In-context Learning as Implicit Bayesian Inference (2021, methodology): “Topic models and HMMs. Topic models such as LDA (Blei et al., 2003) also have document-level latent variables, but learning is typically relies on algorithms such as EM (Dempster et al., 1977), variational inference (Jordan et al., 1999), or MCMC (Hastings, 1970, Metropolis et al., 1953).”
Bayesian forecasting in economics and finance: A modern review (2022, methodology): “Methods in (ii) include Monte Carlo sampling (Metropolis and Ulam, 1949), importance sampling (IS) (Hammersley and Handscomb, 1964; Kloek and van Dijk, 1978; Geweke, 1989) and MCMC sampling – including Gibbs sampling (Geman and Geman, 1984; Gelfand and Smith, 1990) and Metropolis-Hastings (MH) algor”
Light Hadron Masses from Lattice QCD (2012, background): “One can then perform a Metropolis (Metropolis et al., 1953) step, i.e. accept the gauge configuration U ′ as the next gauge configuration in the Markov chain with probability e−δS if the action has increased δS > 0 or with probability 1 otherwise.”
Cobaya: code for Bayesian analysis of hierarchical physical models (2020, methodology): “This includes a Metropolis-Hastings MCMC [19, 20] sampler, with optional oversampling of fast parameters, and a variation of it that performs dragging [1] along the fastest directions.”
Simulations of the galaxy population constrained by observations from z = 3 to the present day: implications for galactic winds and the fate of their ejecta (2012, methodology): “Section 3 of Henriques et al. (2009) gives a full description of the Metropolis–Hastings algorithm used here (Metropolis et al. 1953; Hastings 1970) which builds chains in which each step to a new set of parameters depends only on the current parameter set, not on the previous history.”
Bayesian calibration of building energy models with large datasets (2017, methodology): “Two MCMC algorithms that have been used for the Bayesian calibration of building energy models are the random-walk Metropolis (RWM) (Metropolis et al., 1953) and the Gibbs sampling (Geman and Geman, 1984).”
The uncertainty in Galactic parameters (2009, methodology): “We use a Metropolis algorithm (Metropolis et al. 1953) to identify the peak in this probability distribution, and to characterise its width around the most probable model.”
A Monte Carlo form-finding method for large scale regular and irregular tensegrity structures (2010, methodology): “The Monte Carlo method used in most molecular simulations is based on an accept-or-reject scheme (Metropolis et al., 1953).”
Generating Fluent Adversarial Examples for Natural Languages (2019, methodology): “MHA is an adversarial example generator based on Metropolis-Hastings (M-H) sampling (Metropolis et al., 1953; HASTINGS, 1970; Chib and Greenberg, 1995).”