scholarly journals Use of Rifampin Compared with Isoniazid for the Treatment of Latent Tuberculosis Infection in Japan: A Bayesian Inference with Markov Chain Monte Carlo Method

2020 ◽  
Vol 59 (21) ◽  
pp. 2687-2691 ◽  
Author(s):  
Kentaro Iwata ◽  
Naomi Morishita ◽  
Masami Nishiwaki ◽  
Chisato Miyakoshi
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Bayesian Inference ◽  
Monte Carlo Method ◽  
Latent Tuberculosis Infection ◽  
Latent Tuberculosis ◽  
Tuberculosis Infection

scholarly journals Bayesian inference for multistage and other incomplete designs

2020 ◽  
Author(s):  
Jesse Koops ◽  
Timo Bechger ◽  
Gunter Maris
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Bayesian Inference ◽  
Monte Carlo Method ◽  
Simulated Data ◽  
Operating Characteristics ◽  
Multistage Testing ◽  
Incomplete Designs ◽  
San Martín

Following Maris, Bechger and San-Martin (2015), we develop a Markov chain - Monte Carlo method for Bayesian inference tailored to handle data collected in multistage and other incomplete designs. We illustrate its operating characteristics with simulated data, and provide a real application. To appear as: Koops, J. and Bechger, T. and Maris, G. (2021); Bayesian inference for multistage and other incomplete designs. In Research for Practical Issues and Solutions in Computerized Multistage Testing. Routledge, London.

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