Monte Carlo methods in statistical physics / M.E.J. Newman and G.T. Barkema.

Includes bibliographical references (pages 410-413) and index.

Print version record.

Equilibrium Monte Carlo simulations -- Introduction -- Statistical mechanics -- Equilibrium -- Fluctuations, correlations and responses -- example: the Ising model -- Numerical methods -- Monte Carlo simulation -- brief history of the Monte Carlo method -- Problems -- principles of equilibrium thermal Monte Carlo simulation -- estimator -- Importance sampling -- Markov processes -- Ergodicity -- Detailed balance -- Acceptance ratios -- Continuous time Monte Carlo -- Problems -- Ising model and the Metropolis algorithm -- Metropolis algorithm -- Implementing the Metropolis algorithm -- Equilibration -- Measurement -- Autocorrelation functions -- Correlation times and Markov matrices -- Calculation of errors -- Estimation of statistical errors -- blocking method -- bootstrap method -- jackknife method -- Systematic errors -- Measuring the entropy -- Measuring correlation functions -- actual calculation -- phase transition -- Critical fluctuations and critical showing down -- Problems -- Other algorithms for the Ising model -- Critical exponents and their measurement -- Wolff algorithm -- Acceptance ratio for a cluster algorithm -- Properties of the Wolff algorithm -- correlation time and the dynamic exponent -- dynamic exponent and the susceptibility -- Further algorithms for the Ising model -- Swendsen -- Wang algorithm -- Niedermayer's algorithm -- Multigrid methods -- invaded cluster algorithm -- Other spin models -- Potts models -- Cluster algorithms for Potts models -- Continuous spin models -- Problems -- conserved-order-parameter Ising model -- Kawasaki algorithm -- Simulation of interfaces -- More efficient algorithms -- continuous time algorithm -- Equilibrium crystal shapes -- Problems -- Disordered spin models -- Glassy systems -- random-field Ising model -- Spin glasses

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