Markov chain Monte Carlo analysis of underreported count data with an application to worker absenteeism

1996 ◽  
Vol 21 (4) ◽  
pp. 575-587 ◽  
Author(s):  
Rainer Winkelmann
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Count Data ◽  
Monte Carlo Analysis ◽  
Worker Absenteeism

scholarly journals Markov Chain Monte Carlo Analysis of Correlated Count Data

2001 ◽  
Vol 19 (4) ◽  
pp. 428-435 ◽  
Author(s):  
Siddhartha Chib ◽  
Rainer Winkelmann
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Count Data ◽  
Monte Carlo Analysis

scholarly journals Application of Markov chain Monte Carlo analysis to biomathematical modeling of respirable dust in US and UK coal miners

2013 ◽  
Vol 66 (1) ◽  
pp. 47-58 ◽  
Author(s):  
Lisa M. Sweeney ◽  
Ann Parker ◽  
Lynne T. Haber ◽  
C. Lang Tran ◽  
Eileen D. Kuempel
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Monte Carlo Analysis ◽  
Respirable Dust ◽  
Coal Miners ◽  
Biomathematical Modeling

scholarly journals The effects of LIGO detector noise on a 15-dimensional Markov-chain Monte Carlo analysis of gravitational-wave signals

2010 ◽  
Vol 27 (11) ◽  
pp. 114009 ◽  
Author(s):  
V Raymond ◽  
M V van der Sluys ◽  
I Mandel ◽  
V Kalogera ◽  
C Röver ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Gravitational Wave ◽  
Monte Carlo Analysis ◽  
Detector Noise

scholarly journals Potential sources of red snapper (Lutjanus campechanus) recruits estimated with Markov Chain Monte Carlo analysis of otolith chemical signatures

2016 ◽  
Vol 67 (7) ◽  
pp. 992 ◽  
Author(s):  
Beverly K. Barnett ◽  
William F. Patterson ◽  
Todd Kellison ◽  
Steven B. Garner ◽  
Alan M. Shiller
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Chemical Constituents ◽  
Monte Carlo Analysis ◽  
Red Snapper ◽  
Lutjanus Campechanus ◽  
Age Classes ◽  
Elemental Ratios ◽  
Chemical Signatures

Otolith chemical signatures were used to estimate the number of likely nursery sources that contributed recruits to a suite of red snapper (Lutjanus campechanus) year-classes sampled in 2012 in US Atlantic Ocean waters from southern Florida (28°N) to North Carolina (34°N). Otoliths from subadult and adult fish (n=139; ages 2–5 years) were cored and their chemical constituents analysed for δ13C, δ18O, as well as the elemental ratios of Ba:Ca, Mg:Ca, Mn:Ca and Sr:Ca. Results from multiple linear regression analyses indicated that there was significant latitudinal variation for δ13C, Ba:Ca, Mg:Ca and Mn:Ca. Therefore, these variables were used to parameterise Markov Chain Monte Carlo (MCMC) models computed to estimate the most likely number of nursery sources to each age class. Results from MCMC models indicated that between two and seven nursery sources were equally plausible among the four age classes examined, but the likely number of nursery sources declined for fish aged 4 and 5 years because of apparent mixing between more northern and more southern signatures. Overall, there is evidence to reject the null hypothesis that a single nursery source contributed recruits among the age classes examined, but increased sample size from a broader geographic range may be required to refine estimates of the likely number of nursery sources.

Sensitivity analysis of an urban stormwater microorganism model

2010 ◽  
Vol 62 (6) ◽  
pp. 1393-1400 ◽  
Author(s):  
D. T. McCarthy ◽  
A. Deletic ◽  
V. G. Mitchell ◽  
C. Diaper
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Monte Carlo Analysis ◽  
Parameter Distribution ◽  
Model Parameters ◽  
Urban Stormwater ◽  
Urban Catchments ◽  
Two Parameters

This paper presents the sensitivity analysis of a newly developed model which predicts microorganism concentrations in urban stormwater (MOPUS—MicroOrganism Prediction in Urban Stormwater). The analysis used Escherichia coli data collected from four urban catchments in Melbourne, Australia. The MICA program (Model Independent Markov Chain Monte Carlo Analysis), used to conduct this analysis, applies a carefully constructed Markov Chain Monte Carlo procedure, based on the Metropolis-Hastings algorithm, to explore the model's posterior parameter distribution. It was determined that the majority of parameters in the MOPUS model were well defined, with the data from the MCMC procedure indicating that the parameters were largely independent. However, a sporadic correlation found between two parameters indicates that some improvements may be possible in the MOPUS model. This paper identifies the parameters which are the most important during model calibration; it was shown, for example, that parameters associated with the deposition of microorganisms in the catchment were more influential than those related to microorganism survival processes. These findings will help users calibrate the MOPUS model, and will help the model developer to improve the model, with efforts currently being made to reduce the number of model parameters, whilst also reducing the slight interaction identified.

scholarly journals Markov chain Monte Carlo analysis for the selection of a cell-killing model under high-dose-rate irradiation

2017 ◽  
Vol 44 (10) ◽  
pp. 5522-5532 ◽  
Author(s):  
Yusuke Matsuya ◽  
Takaaki Kimura ◽  
Hiroyuki Date
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Dose Rate ◽  
Monte Carlo Analysis ◽  
High Dose Rate ◽  
Cell Killing ◽  
High Dose ◽  
A Cell ◽  
Selection Of
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