Titlebook: Markov Chain Monte Carlo Methods in Quantum Field Theories; A Modern Primer Anosh Joseph Book 2020 The Author(s), under exclusive license t

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Hybrid (Hamiltonian) Monte Carlo, motion, and it can be formalized in an elegant way using Hamiltonian dynamics. The two approaches, statistical (MCMC) and deterministic (molecular dynamics), coexisted peacefully for a long time. In 1987, an extraordinary paper by Duane et al. [15] combined the MCMC and molecular dynamics approaches. They called their method . (HMC).

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2191-5423 lines on how to avoid common pitfalls while applying Monte C.This primer is a comprehensive collection of analytical and numerical techniques that can be used to extract the non-perturbative physics of quantum field theories. The intriguing connection between Euclidean Quantum Field Theories (QFTs)

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Book 2020field theories. The intriguing connection between Euclidean Quantum Field Theories (QFTs) and statistical mechanics can be used to apply Markov Chain Monte Carlo (MCMC) methods to investigate strongly coupled QFTs. The overwhelming amount of reliable results coming from the field of lattice quantum

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https://doi.org/10.1007/978-3-030-46044-0Monte Carlo Simulation; Lattice Field Theory; Non-perturbative Field Theory; Strongly Coupled Field The

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Monte Carlo with Importance Sampling,In this chapter we discuss a method that can increase the efficiency of Monte Carlo integration. This technique is called importance sampling. It is one of the several available . techniques, in the context of Monte Carlo integration.

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Markov Chains,In the previous chapter we looked at Monte Carlo integration methods that employ naive sampling and importance sampling. There, we used a uniform random sampling method with or without a weight function to find the integral of a ‘well-behaved’ function.

Introduction

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