Damage Identification for Masonry Materials Based on Bayesian Inference

2013 ◽  
Vol 405-408 ◽  
pp. 2498-2502
Author(s):  
Xiang Ping Fu ◽  
Bin Peng ◽  
Zheng Ji
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Sampling Method ◽  
Damage Identification ◽  
Dynamic Test ◽  
Finite Element Models ◽  
Masonry Structures ◽  
Dynamic Tests ◽  
Bayesian Inferences ◽  
Mcmc Sampling

The basic frequency of masonry specimens can be obtained by dynamic tests with ambient or artificial excitation. The elastic modulus of masonry structures, as well as the damage factors, can then be determined by training their finite element models and make the calculated frequencies agree with the measured ones. Using 530 groups of dynamic test data, the damage factors of four masonry specimens were identified. The Bayesian inferences of the highly diverse measured results were conducted through a Markov Chain Monte Carlo (MCMC) sampling method, and the location of the damage was identified. The methodology was applicable, and can be used in the damage identification for other materials or structures.

scholarly journals Assessing a Bayesian Embedding Approach to Circular Regression Models

Methodology ◽  
2018 ◽  
Vol 14 (2) ◽  
pp. 69-81 ◽  
Author(s):  
Jolien Cremers ◽  
Tim Mainhard ◽  
Irene Klugkist
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Regression Models ◽  
Sampling Method ◽  
Circular Data ◽  
Interpersonal Circumplex ◽  
Simulation Studies ◽  
Mcmc Sampling ◽  
Circular Regression

Abstract. Circular data is different from linear data and its analysis also requires methods different from conventional methods. In this study a Bayesian embedding approach to estimating circular regression models is investigated, by means of simulation studies, in terms of performance, efficiency, and flexibility. A new Markov chain Monte Carlo (MCMC) sampling method is proposed and contrasted to an existing method. An empirical example of a regression model predicting teachers’ scores on the interpersonal circumplex will be used throughout. Performance and efficiency are better for the newly proposed sampler and reasonable to good in most situations. Furthermore, the method in general is deemed very flexible. Additional research should be done that provides an overview of what circular data looks like in practice, investigates the interpretation of the circular effects and examines how we might conduct a way of hypothesis testing or model checking for the embedding approach.

scholarly journals Convergence of Conditional Metropolis-Hastings Samplers

2014 ◽  
Vol 46 (2) ◽  
pp. 422-445 ◽  
Author(s):  
Galin L. Jones ◽  
Gareth O. Roberts ◽  
Jeffrey S. Rosenthal
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Diffusion Process ◽  
Sample Path ◽  
Discrete Observations ◽  
Entire Sample ◽  
Bayesian Inferences ◽  
Monte Carlo Algorithms

We consider Markov chain Monte Carlo algorithms which combine Gibbs updates with Metropolis-Hastings updates, resulting in a conditional Metropolis-Hastings sampler (CMH sampler). We develop conditions under which the CMH sampler will be geometrically or uniformly ergodic. We illustrate our results by analysing a CMH sampler used for drawing Bayesian inferences about the entire sample path of a diffusion process, based only upon discrete observations.

scholarly journals Robust and fast Monte Carlo Markov Chain sampling of diffusion MRI microstructure models

2018 ◽  
Author(s):  
R.L. Harms ◽  
A. Roebroeck
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Sample Size ◽  
Diffusion Mri ◽  
Monte Carlo Markov Chain ◽  
Point Estimate ◽  
Model Parameters ◽  
Effective Sample Size ◽  
Compartment Models ◽  
Mcmc Sampling

AbstractIn diffusion MRI analysis, advances in biophysical multi-compartment modeling have gained popularity over the conventional Diffusion Tensor Imaging (DTI), because they possess greater specificity in relating the dMRI signal to underlying cellular microstructure. Biophysical multi-compartment models require parameter estimation, typically performed using either Maximum Likelihood Estimation (MLE) or using Monte Carlo Markov Chain (MCMC) sampling. Whereas MLE provides only a point estimate of the fitted model parameters, MCMC recovers the entire posterior distribution of the model parameters given the data, providing additional information such as parameter uncertainty and correlations. MCMC sampling is currently not routinely applied in dMRI microstructure modeling because it requires adjustments and tuning specific to each model, particularly in the choice of proposal distributions, burn-in length, thinning and the number of samples to store. In addition, sampling often takes at least an order of magnitude more time than non-linear optimization. Here we investigate the performance of MCMC algorithm variations over multiple popular diffusion microstructure models to see whether a single well performing variation could be applied efficiently and robustly to many models. Using an efficient GPU-based implementation, we show that run times can be removed as a prohibitive constraint for sampling of diffusion multi-compartment models. Using this implementation, we investigated the effectiveness of different adaptive MCMC algorithms, burn-in, initialization and thinning. Finally we apply the theory of Effective Sample Size to diffusion multi-compartment models as a way of determining a relatively general target for the number of samples needed to characterize parameter distributions for different models and datasets. We conclude that robust and fast sampling is achieved in most diffusion microstructure models with the Adaptive Metropolis-Within-Gibbs (AMWG) algorithm initialized with an MLE point estimate, in which case 100 to 200 samples are sufficient as a burn-in and thinning is mostly unnecessary. As a relatively general target for the number of samples, we recommend a multivariate Effective Sample Size of 2200.

Multiple Damage Identification Using the Reversible Jump Markov Chain Monte Carlo

2015 ◽  
Author(s):  
DANIELA TIBOACA ◽  
ROBERT BARTHORPE ◽  
IFIGENEIA ANTONIADOU ◽  
KEITH WORDEN
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Damage Identification ◽  
Reversible Jump

scholarly journals Convergence of Conditional Metropolis-Hastings Samplers

2014 ◽  
Vol 46 (02) ◽  
pp. 422-445 ◽  
Author(s):  
Galin L. Jones ◽  
Gareth O. Roberts ◽  
Jeffrey S. Rosenthal
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Diffusion Process ◽  
Sample Path ◽  
Discrete Observations ◽  
Entire Sample ◽  
Bayesian Inferences ◽  
Monte Carlo Algorithms

We consider Markov chain Monte Carlo algorithms which combine Gibbs updates with Metropolis-Hastings updates, resulting in aconditional Metropolis-Hastings sampler(CMH sampler). We develop conditions under which the CMH sampler will be geometrically or uniformly ergodic. We illustrate our results by analysing a CMH sampler used for drawing Bayesian inferences about the entire sample path of a diffusion process, based only upon discrete observations.

scholarly journals Robust Constraint of Luminosity Function Evolution through MCMC Sampling

2014 ◽  
Vol 10 (S306) ◽  
pp. 295-297 ◽  
Author(s):  
Noah Kurinsky ◽  
Anna Sajina
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Galaxy Evolution ◽  
Luminosity Function ◽  
Test Case ◽  
Simulation Methodology ◽  
Galaxy Surveys ◽  
Mcmc Sampling ◽  
Initial Results ◽  
Simulation Package

AbstractWe present a new galaxy survey simulation package, which combines the power of Markov Chain Monte Carlo (MCMC) sampling with a robust and adaptable model of galaxy evolution. The aim of this code is to aid in the characterization and study of new and existing galaxy surveys. In this paper we briefly describe the MCMC implementation and the survey simulation methodology and associated tools. A test case of this full suite was to constrain the evolution of the IR Luminosity Function (LF) based on the HerMES (Herschel SPIRE) survey of the Spitzer First Look Survey field. The initial results are consistent with previous studies, but our more general approach should be of wider benefit to the community.

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