Book contents
- A Guide to Monte Carlo Simulations in Statistical Physics
- A Guide to Monte Carlo Simulations in Statistical Physics
- Copyright page
- Contents
- Preface
- 1 Introduction
- 2 Some necessary background
- 3 Simple sampling Monte Carlo methods
- 4 Importance sampling Monte Carlo methods
- 5 More on importance sampling Monte Carlo methods for lattice systems
- 6 Off-lattice models
- 7 Reweighting methods
- 8 Quantum Monte Carlo methods
- 9 Monte Carlo renormalization group methods
- 10 Non-equilibrium and irreversible processes
- 11 Lattice gauge models: a brief introduction
- 12 A brief review of other methods of computer simulation
- 13 Monte Carlo simulations at the periphery of physics and beyond
- 14 Monte Carlo studies of biological molecules
- 15 Emerging trends
- Index
- References
7 - Reweighting methods
Published online by Cambridge University Press: 24 November 2021
- A Guide to Monte Carlo Simulations in Statistical Physics
- A Guide to Monte Carlo Simulations in Statistical Physics
- Copyright page
- Contents
- Preface
- 1 Introduction
- 2 Some necessary background
- 3 Simple sampling Monte Carlo methods
- 4 Importance sampling Monte Carlo methods
- 5 More on importance sampling Monte Carlo methods for lattice systems
- 6 Off-lattice models
- 7 Reweighting methods
- 8 Quantum Monte Carlo methods
- 9 Monte Carlo renormalization group methods
- 10 Non-equilibrium and irreversible processes
- 11 Lattice gauge models: a brief introduction
- 12 A brief review of other methods of computer simulation
- 13 Monte Carlo simulations at the periphery of physics and beyond
- 14 Monte Carlo studies of biological molecules
- 15 Emerging trends
- Index
- References
Summary
One longstanding limitation on the resolution of Monte Carlo simulations near phase transitions has been the need to perform many runs to precisely characterize peaks in response functions such as the specific heat. Dramatic improvements have become possible with the realization that entire distributions of properties, not just mean values, can be useful; in particular, they can be used to predict the behavior of the system at a temperature other than that at which the simulation was performed. There are several different ways in which this may be done. The reweighting may be done after a simulation is complete or it may become an integral part of the simulation process itself. The fundamental basis for this approach is the realization that the properties of the systems will be determined by a distribution function in an appropriate ensemble.
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- A Guide to Monte Carlo Simulations in Statistical Physics , pp. 326 - 364Publisher: Cambridge University PressPrint publication year: 2021