The estimation of lower refractivity uncertainty from radar sea clutter using the Bayesian—MCMC method

2013 ◽  
Vol 22 (2) ◽  
pp. 029302 ◽  
Author(s):  
Zheng Sheng
Author(s):  
Yibing Wang ◽  
Xueling Qu ◽  
Haitao Wang

Background: Entrepreneurs not only promote a nation’s economic growth but also increase employment. The risk of obesity among entrepreneurs may bring heavy economic burdens not only to the entrepreneurs but also to the national health care system. We aimed to examine the association between entrepreneurship and the risk of obesity. Methods: We utilized data from the 2015 Harmonized China Health and Retirement Longitudinal Survey, including 2,802 individuals aged between 45 and 65 with complete data. This study used BMI (Body Mass Index) (kg/m2 ) as an indicator of obesity risk. Entrepreneurs were defined as those respondents who run their own businesses as main jobs. We used multivariate OLS regression models and Bayesian Markov Chain Monte Carlo (MCMC) method to examine the link of entrepreneurship and obesity risk. Results: The multivariate OLS regression results showed that entrepreneurship was positively associated with BMI (P<0.01). The Bayesian MCMC results indicated that the posterior mean was (0.597, 90% HPD CI: 0.319, 0.897), demonstrating that entrepreneurship was indeed significantly positively associated with the risk of obesity. Conclusion: Being an entrepreneur is positively associated with the risk of obesity. As obesity can cause diseases such as hypertension, diabetes, coronary heart disease and stroke, the health departments should take necessary health interventions to prevent entrepreneurs from being obese in order to increase their entrepreneurial success.


2020 ◽  
Author(s):  
Mehrdad Pakzad ◽  
Mahnaz Khalili ◽  
Shaghayegh Vahidravesh

Abstract. Monte Carlo Markov chain (MCMC) samplings can obtain a set of samples by directed random walk, mapping the posterior probability density of the model parameters in Bayesian framework. We perform earthquake waveform inversion to retrieve focal angles or the elements of moment tensor and source location using a Bayesian MCMC method with the constraints of first-motion polarities and double couple percentage using full Green functions and data covariance matrix. The algorithm tests the compatibility with polarities and also checks the double couple percentage of every site before the time-consuming synthetic seismogram computation for every sample of moment tensor of every trial source position. Other than large earthquakes, the method is especially suitable for weak events (M 


Author(s):  
Sabrina Machhour ◽  
Stephane Kemkemian ◽  
Pierre-Albert Breton ◽  
Vincent Corretja
Keyword(s):  

2011 ◽  
Vol 33 (8) ◽  
pp. 1786-1791 ◽  
Author(s):  
Wei Jiang ◽  
Wei-bo Deng ◽  
Qiang Yang
Keyword(s):  

2007 ◽  
Author(s):  
Sandeep P. Sira ◽  
Antonia Papandreou-Suppappola ◽  
Darryl Morrell ◽  
Douglas Cochran
Keyword(s):  

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