2020
DOI: 10.1007/978-3-030-46044-0
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Markov Chain Monte Carlo Methods in Quantum Field Theories

Abstract: We introduce and discuss Monte Carlo methods in quantum field theories. Methods of independent Monte Carlo, such as random sampling and importance sampling, and methods of dependent Monte Carlo, such as Metropolis sampling and Hamiltonian Monte Carlo, are introduced. We review the underlying theoretical foundations of Markov chain Monte Carlo. We provide several examples of Monte Carlo simulations, including one-dimensional simple harmonic oscillator, unitary matrix model exhibiting Gross-Witten-Wadia transiti… Show more

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Cited by 14 publications
(11 citation statements)
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“…which is thus consistent with the analytic estimate (6.1). 15 For other papers using MC methods in matrix models see, e.g., [36][37][38][39][40][41] and section 6.5 of [42]. In the explored region of parameter space the Metropolis-Hastings algorithm turned out to be faster than the Hybrid Monte Carlo one, taking into account autocorrelation.…”
Section: Orbifold Theorymentioning
confidence: 99%
“…which is thus consistent with the analytic estimate (6.1). 15 For other papers using MC methods in matrix models see, e.g., [36][37][38][39][40][41] and section 6.5 of [42]. In the explored region of parameter space the Metropolis-Hastings algorithm turned out to be faster than the Hybrid Monte Carlo one, taking into account autocorrelation.…”
Section: Orbifold Theorymentioning
confidence: 99%
“…15 For other papers using MC methods in matrix models see, e.g., [36,37,38,39,40,41] and section 6.5 of [42]. In the explored region of parameter space the Metropolis-Hastings algorithm turned out to be faster than the Hybrid Monte Carlo one, taking into account autocorrelation.…”
Section: Orbifold Theorymentioning
confidence: 99%
“…For a detailed review about HMC and its extension to rational HMC which is required for fermions, the interested readers can consult Ref. [25,26]. The two basic parts of HMC are, a) Use of integrator to evolve and propose a new configuration, b) accept or reject the proposed configuration.…”
Section: Basics Of Monte Carlo Methodsmentioning
confidence: 99%