Non-stationary Bayesian estimation of parameters from a body cover model of the vocal folds

2016 ◽  
Vol 139 (5) ◽  
pp. 2683-2696 ◽  
Author(s):  
Paul J. Hadwin ◽  
Gabriel E. Galindo ◽  
Kyle J. Daun ◽  
Matías Zañartu ◽  
Byron D. Erath ◽  
...  
Keyword(s):  
Bayesian Estimation ◽  
Vocal Folds ◽  
Estimation Of Parameters

scholarly journals Statistical Inference for the Weibull Distribution Based on δ-Record Data

Symmetry ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 20 ◽  
Author(s):  
Raúl Gouet ◽  
F. Javier López ◽  
Lina Maldonado ◽  
Gerardo Sanz
Keyword(s):  
Maximum Likelihood ◽  
Experimental Design ◽  
Weibull Distribution ◽  
Bayesian Estimation ◽  
Statistical Inference ◽  
Numerical Computation ◽  
Weibull Model ◽  
Rainfall Series ◽  
Estimation Of Parameters ◽  
Record Data

We consider the maximum likelihood and Bayesian estimation of parameters and prediction of future records of the Weibull distribution from δ -record data, which consists of records and near-records. We discuss existence, consistency and numerical computation of estimators and predictors. The performance of the proposed methodology is assessed by Montecarlo simulations and the analysis of monthly rainfall series. Our conclusion is that inferences for the Weibull model, based on δ -record data, clearly improve inferences based solely on records. This methodology can be recommended, more so as near-records can be collected along with records, keeping essentially the same experimental design.

scholarly journals Bayesian Inference of Vocal Fold Material Properties from Glottal Area Waveforms Using a 2D Finite Element Model

2019 ◽  
Vol 9 (13) ◽  
pp. 2735 ◽  
Author(s):  
Paul J. Hadwin ◽  
Mohsen Motie-Shirazi ◽  
Byron D. Erath ◽  
Sean D. Peterson
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Bayesian Estimation ◽  
Material Properties ◽  
Vocal Fold ◽  
Vocal Folds ◽  
Element Model ◽  
Lumped Element ◽  
Subglottal Pressure ◽  
Contact Pressures

Bayesian estimation has been previously demonstrated as a viable method for developing subject-specific vocal fold models from observations of the glottal area waveform. These prior efforts, however, have been restricted to lumped-element fitting models and synthetic observation data. The indirect relationship between the lumped-element parameters and physical tissue properties renders extracting the latter from the former difficult. Herein we propose a finite element fitting model, which treats the vocal folds as a viscoelastic deformable body comprised of three layers. Using the glottal area waveforms generated by self-oscillating silicone vocal folds we directly estimate the elastic moduli, density, and other material properties of the silicone folds using a Bayesian importance sampling approach. Estimated material properties agree with the “ground truth” experimental values to within 3 % for most parameters. By considering cases with varying subglottal pressure and medial compression we demonstrate that the finite element model coupled with Bayesian estimation is sufficiently sensitive to distinguish between experimental configurations. Additional information not available experimentally, namely, contact pressures, are extracted from the developed finite element models. The contact pressures are found to increase with medial compression and subglottal pressure, in agreement with expectation.

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