Markov Chain Monte Carlo: Innovations And Applications by W. S. Kendall, J. S. Wang

By W. S. Kendall, J. S. Wang

Markov Chain Monte Carlo (MCMC) originated in statistical physics, yet has spilled over into numerous program components, resulting in a corresponding number of ideas and techniques. That style stimulates new principles and advancements from many various locations, and there's a lot to be received from cross-fertilization. This booklet provides 5 expository essays by means of leaders within the box, drawing from views in physics, information and genetics, and exhibiting how assorted points of MCMC come to the fore in numerous contexts. The essays derive from instructional lectures at an interdisciplinary application on the Institute for Mathematical Sciences, Singapore, which exploited the fascinating ways that MCMC spreads throughout assorted disciplines.

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Extra resources for Markov Chain Monte Carlo: Innovations And Applications (Lecture Notes Series, Institute for Mathematical Sciences, N)

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4. Energy histograms of 100 000 entries each for the Ising model on a 20×20 lattice: Random Sampling gives statistically independent configurations at β = 0. 4 are generated with Markov chain MC. 2 is shown to fail (assignments a0301 02 and a0303 02). by Ferrenberg and Swendsen [12] (accurate determinations of peaks of the specific heat or of susceptibilities). In Fig. 2. 2, the result is seen to be disastrous. 2 histogram takes on its maximum, the β = 0 histogram has not a single entry. 2. Re-weighting to new β values works only in a range β0 ± △β, where △β → 0 in the infinite volume limit.

The Metropolis process introduces an autocorrelation time in the generation of normally distributed random data. We work with N = 217 = 131072 data and take a = 3 for the Markov process (105), what gives an acceptance rate of approximately 50%. The autocorrelation function of this process is depicted in Fig. 9 (assignment a0401 01). The integrated autocorrelation time (assignment a0401 02) is Nb estimators with the direct estimators shown in Fig. 10. We compare the τint τint (t) at t = Nb − 1 .

A. Berg 38 to allow the system to settle down. That is a first reason, why it appears necessary to control the integrated autocorrelation time of a MC simulation. A second reason is that we have to control the error bars of the equilibrium part of our simulation. Ideally the error bars are calculated as △f = σ 2 (f ) with σ 2 (f ) = τint σ 2 (f ) . N (115) This constitutes a self-consistent error analysis of a MC simulation. However, the calculation of the integrated autocorrelation time may be out of reach.

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