scholarly journals Optimizing and Adapting the Metropolis Algorithm

2014 ◽  
pp. 117-132 ◽  
Keyword(s):  
Metropolis Algorithm

scholarly journals Marshall Rosenbluth and the Metropolis algorithm

2005 ◽  
Vol 12 (5) ◽  
pp. 057303 ◽  
Author(s):  
J. E. Gubernatis
Keyword(s):  
Metropolis Algorithm

scholarly journals Asymptotic analysis of the random walk Metropolis algorithm on ridged densities

2018 ◽  
Vol 28 (5) ◽  
pp. 2966-3001 ◽  
Author(s):  
Alexandros Beskos ◽  
Gareth Roberts ◽  
Alexandre Thiery ◽  
Natesh Pillai
Keyword(s):  
Random Walk ◽  
Asymptotic Analysis ◽  
Metropolis Algorithm ◽  
Random Walk Metropolis ◽  
Random Walk Metropolis Algorithm

scholarly journals Constraining ecosystem model with Adaptive Metropolis algorithm using boreal forest site eddy covariance measurements

2016 ◽  
Author(s):  
Jarmo Mäkelä ◽  
Jouni Susiluoto ◽  
Tiina Markkanen ◽  
Mika Aurela ◽  
Ivan Mammarella ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Gross Primary Production ◽  
Co2 Concentration ◽  
Moisture Stress ◽  
Ecosystem Model ◽  
Metropolis Algorithm ◽  
Forest Site ◽  
Diurnal Cycles ◽  
Forest Sites ◽  
Eddy Covariance Measurements

Abstract. We examined parameter optimization in JSBACH ecosystem model, applied for two boreal forest sites in Finland. We identified and tested key parameters in soil hydrology and forest water and carbon exchange related formulations and optimized them using the Adaptive Metropolis algorithm for a five year calibration period (2000–2004) followed by a four year validation period (2005–2008). We were able to improve the modelled seasonal, daily and diurnal cycles of gross primary production and evapotranspiration but unable to enhance the models response to dryness. The improvements are mostly accounted for by parameters related to the ratio of leaf internal CO2 concentration to external CO2, relative humidity, transpiration and soil moisture stress.

The Shark Attack Problem Revisited: MCMC with the Metropolis Algorithm

2019 ◽  
pp. 193-211
Author(s):  
Therese M. Donovan ◽  
Ruth M. Mickey
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Markov Chain ◽  
Posterior Distribution ◽  
Metropolis Algorithm ◽  
Tuning Parameter ◽  
The Mean ◽  
Shark Attacks ◽  
Mcmc Approach

In this chapter, the “Shark Attack Problem” (Chapter 11) is revisited. Markov Chain Monte Carlo (MCMC) is introduced as another way to determine a posterior distribution of λ‎, the mean number of shark attacks per year. The MCMC approach is so versatile that it can be used to solve almost any kind of parameter estimation problem. The chapter highlights the Metropolis algorithm in detail and illustrates its application, step by step, for the “Shark Attack Problem.” The posterior distribution generated in Chapter 11 using the gamma-Poisson conjugate is compared with the MCMC posterior distribution to show how successful the MCMC method can be. By the end of the chapter, the reader should also understand the following concepts: tuning parameter, MCMC inference, traceplot, and moment matching.

scholarly journals Experimental Validation of Optimal Parameter and Uncertainty Estimation for Structural Systems Using a Shuffled Complex Evolution Metropolis Algorithm

2019 ◽  
Vol 9 (22) ◽  
pp. 4959 ◽  
Author(s):  
Hesheng Tang ◽  
Xueyuan Guo ◽  
Liyu Xie ◽  
Songtao Xue
Keyword(s):  
Optimal Parameter ◽  
Structural Parameters ◽  
Metropolis Algorithm ◽  
Shaking Table ◽  
Parameter Distribution ◽  
Physical Parameters ◽  
Structural Systems ◽  
Model Errors ◽  
Nonlinear Structural ◽  
Shuffled Complex Evolution

The uncertainty in parameter estimation arises from structural systems’ input and output measured errors and from structural model errors. An experimental verification of the shuffled complex evolution metropolis algorithm (SCEM-UA) for identifying the optimal parameters of structural systems and estimating their uncertainty is presented. First, the estimation framework is theoretically developed. The SCEM-UA algorithm is employed to search through feasible parameters’ space and to infer the posterior distribution of the parameters automatically. The resulting posterior parameter distribution then provides the most likely estimation of parameter sets that produces the best model performance. The algorithm is subsequently validated through both numerical simulation and shaking table experiment for estimating the parameters of structural systems considering the uncertainty of available information. Finally, the proposed algorithm is extended to identify the uncertain physical parameters of a nonlinear structural system with a particle mass tuned damper (PTMD). The results demonstrate that the proposed algorithm can effectively estimate parameters with uncertainty for nonlinear structural systems, and it has a stronger anti-noise capability. Notably, the SCEM-UA method not only shows better global optimization capability compared with other heuristic optimization methods, but it also has the ability to simultaneously estimate the uncertainties associated with the posterior distributions of the structural parameters within a single optimization run.

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