scholarly journals Fear Generalization of Emotional Stimuli Can Be Explained By a Bayesian Inference Model

2019 ◽  
Author(s):  
Lukas Neugebauer ◽  
Christian Büchel
Keyword(s):  
Bayesian Inference ◽  
Emotional Stimuli ◽  
Inference Model ◽  
Fear Generalization

Impact of the Uncertainties of the RAOs of a Semisubmersible Platform on the Performance of a Motion-Based Wave Inference Method

2019 ◽  
Author(s):  
J. Mas-Soler ◽  
Pedro C. de Mello ◽  
Eduardo A. Tannuri ◽  
Alexandre N. Simos ◽  
A. Souto-Iglesias
Keyword(s):  
Bayesian Inference ◽  
Transfer Functions ◽  
Inference Model ◽  
Sea State ◽  
Statistical Errors ◽  
Regularization Parameters ◽  
Ill Posed ◽  
State Estimations ◽  
The Impact ◽  
Mathematical Process

Abstract Motion based wave inference allows the estimation of the directional sea spectrum from the measured motions of a vessel. Solving the resulting inverse problem is challenging as it is often ill-posed; as a matter of fact, statistical errors of the estimated platform response functions (RAOs) may lead to misleading estimations of the sea states as many noise values are severely amplified in the mathematical process. Hence, in order to obtain reliable estimations of the sea conditions some hypothesis must be included by means of regularization parameters. This work discusses how these errors affect the regularization parameters and the accuracy of the sea state estimations. For this purpose, a statistical quantification of the errors associated to the estimated transfer functions has been included in an expanded Bayesian inference approach. Then, the resulting statistical inference model has been verified by means of a comparison between the outputs of this approach and those obtained without considering the statistical errors in the Bayesian inference. The assessment of the impact on the accuracy of the estimations is based on the results of a dedicated model-scale experimental campaign, which includes more than 150 different test conditions.

Inter-industry wage inequality: persistent differences and turbulent equalisation

2020 ◽  
Vol 44 (4) ◽  
pp. 919-942
Author(s):  
Patrick Mokre ◽  
Miriam Rehm
Keyword(s):  
Economic Theory ◽  
Bayesian Inference ◽  
Wage Inequality ◽  
Fixed Effects ◽  
Wage Differentials ◽  
Wage Determination ◽  
Inference Model ◽  
Hierarchical Bayesian Inference ◽  
Industrial Wage ◽  
Profit Rates

Abstract The empirical stylised fact of persistent inter-industry wage differentials is an enduring challenge to economic theory. This paper applies the classical theory of ‘real competition’ to the turbulent dynamics of these inter-industrial wage differentials. Theoretically, we argue that competitive wage determination can be decomposed into equalising, dispersing and turbulently equalising factors. Empirically, we show graphically and econometrically for 31 US industries in 1987–2016 that wage differentials, like regulating profit rates, are governed by turbulent equalisation. Furthermore, we apply a fixed-effects OLS as well as a hierarchical Bayesian inference model and find that the link between regulating profit rates and wage differentials is positive, significant and robust.

scholarly journals The ALHAMBRA survey: tight dependence of the optical mass-to-light ratio on galaxy colour up to z = 1.5

2019 ◽  
Vol 622 ◽  
pp. A51 ◽  
Author(s):  
C. López-Sanjuan ◽  
L. A. Díaz-García ◽  
A. J. Cenarro ◽  
A. Fernández-Soto ◽  
K. Viironen ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Rest Frame ◽  
Inference Model ◽  
Derived Relations ◽  
Star Forming ◽  
Versus Colour ◽  
Stellar Masses ◽  
Tight Correlation ◽  
Local Results ◽  
Ratio Versus

Aims. Our goal is to characterise the dependence of the optical mass-to-light ratio on galaxy colour up to z = 1.5, expanding the redshift range explored in previous work. Methods. From the redshifts, stellar masses, and rest-frame luminosities of the ALHAMBRA multi-filter survey, we derive the mass-to-light ratio versus colour relation for quiescent and for star-forming galaxies. The intrinsic relation and its physical dispersion are derived with a Bayesian inference model. Results. The rest-frame i-band mass-to-light ratio of quiescent and star-forming galaxies presents a tight correlation with the rest-frame (g − i) colour up to z = 1.5. The mass-to-light ratio versus colour relation is linear for quiescent galaxies and quadratic for star-forming galaxies. The intrinsic dispersion in these relations is 0.02 dex for quiescent galaxies and 0.06 dex for star-forming ones. The derived relations do not present a significant redshift evolution and are compatible with previous local results in the literature. Finally, these tight relations also hold for g- and r-band luminosities. Conclusions. The derived mass-to-light ratio versus colour relations in ALHAMBRA can be used to predict the mass-to-light ratio from a rest-frame optical colour up to z = 1.5. These tight correlations do not change with redshift, suggesting that galaxies have evolved along the derived relations during the last 9 Gyr.

A Bayesian inference model for speech localization (L)

2012 ◽  
Vol 132 (3) ◽  
pp. 1257-1260 ◽  
Author(s):  
José Escolano ◽  
José M. Perez-Lorenzo ◽  
Ning Xiang ◽  
Máximo Cobos ◽  
José J. López
Keyword(s):  
Bayesian Inference ◽  
Inference Model
Sign in / Sign up

Export Citation Format

Share Document