Monte Carlo sampling technique

As the appropriate Boltzmann weights are included in the Metropolis Monte Carlo sampling technique, the average value of the polarizability, or any other property calculated from the MC data, is given as a simple average over all the values calculated for each configuration. 

Combination of RIS with Monte Carlo sampling techniques allows one to determine properties like ... 

Holrnes EW, Kahn SE Molnar PA, Bermes EW Jr. Verification of reference ranges by using a Monte Carlo sampling technique. CUn Chem 1994 40 2216-22. 

Lattice models have proved to be extremely useful in studies of simple, exact models and somewhat more complex models of protein-like systems. Similarly, the conformational space of a real protein could be discretized and explored in a very efficient way by various versions of Monte Carlo sampling techniques. Depending on the assumed level of discretization and model of force field, various levels of accuracy can be achieved. For example, simple lattice models of real proteins were studied by Ueda et al. 

In this section we discuss a different approach that uses Monte Carlo sampling techniques to solve stochastic optimization problems. 

H. Kahn, Use of different Monte Carlo sampling techniques. Symposium on Monte Carlo Methods, University of Florida, March, 1954, John Wiley and Sons, p. 146. 

KAHN, M., Use of Different Monte Carlo Sampling Techniques Symposium on Monte Carlo Methods, M.A. Mayer (ed.), Wiley, New York, 1956, 146-190. 

Risk assessment is proposed by quantifying the usual risk metrics adopted in analyzing CT changes in the literature. It is proposed as a treatment of model and parameter uncertainties based on traditional sensitivity studies and uncertainty assessment respectively, the latter being based on the probabilistic approach for uncertainty formulation and propagation as set by the standard Monte Carlo Sampling technique. 

The PIQMC method is the result of coupling of Feynman s path integral formulation of quantum mechanics with Monte Carlo sampling techniques to produce a method for finite temperature quantum systems. The calculations are not much more complicated than DQMC and produce a sum over all possible states occupied as for a Boltzmann distribution. In the limit of zero temperature... 

Statistical thermal design uncertainty was evaluated by a Monte Carlo sampling technique combined with the subchannel analysis code. The engineering imcer-tainty of the Super LWR is evaluated as 31.88°C based oti the Monte Carlo Statistical Thermal Design Procedure. 

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