scholarly journals Population-based variance reduction for dynamic Monte Carlo

2020 ◽  
Vol 149 ◽  
pp. 107752
Author(s):  
David Legrady ◽  
Mate Halasz ◽  
Jozsef Kophazi ◽  
Balazs Molnar ◽  
Gabor Tolnai
Keyword(s):  
Monte Carlo ◽  
Variance Reduction ◽  
Population Based ◽  
Dynamic Monte Carlo

Variance Reduction for Monte Carlo Methods

2018 ◽  
Vol 482 (6) ◽  
pp. 627-630
Author(s):  
D. Belomestny ◽  
◽  
L. Iosipoi ◽  
N. Zhivotovskiy ◽  
◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Methods ◽  
Variance Reduction

scholarly journals A Bayesian approach to estimating the population prevalence of mood and anxiety disorders using multiple measures

2021 ◽  
Vol 30 ◽  
Author(s):  
Jordan Edwards ◽  
A. Demetri Pananos ◽  
Amardeep Thind ◽  
Saverio Stranges ◽  
Maria Chiu ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Anxiety Disorders ◽  
Administrative Data ◽  
Population Based ◽  
Accurate Estimate ◽  
Service Planning ◽  
Prevalence Estimate ◽  
Population Prevalence ◽  
Mood And Anxiety Disorders ◽  
Prevalence Estimates

Abstract Aims There is currently no universally accepted measure for population-based surveillance of mood and anxiety disorders. As such, the use of multiple linked measures could provide a more accurate estimate of population prevalence. Our primary objective was to apply Bayesian methods to two commonly employed population measures of mood and anxiety disorders to make inferences regarding the population prevalence and measurement properties of a combined measure. Methods We used data from the 2012 Canadian Community Health Survey – Mental Health linked to health administrative databases in Ontario, Canada. Structured interview diagnoses were obtained from the survey, and health administrative diagnoses were identified using a standardised algorithm. These two prevalence estimates, in addition to data on the concordance between these measures and prior estimates of their psychometric properties, were used to inform our combined estimate. The marginal posterior densities of all parameters were estimated using Hamiltonian Monte Carlo (HMC), a Markov Chain Monte Carlo technique. Summaries of posterior distributions, including the means and 95% equally tailed posterior credible intervals, were used for interpretation of the results. Results The combined prevalence mean was 8.6%, with a credible interval of 6.8–10.6%. This combined estimate sits between Bayesian-derived prevalence estimates from administrative data-derived diagnoses (mean = 7.4%) and the survey-derived diagnoses (mean = 13.9%). The results of our sensitivity analysis suggest that varying the specificity of the survey-derived measure has an appreciable impact on the combined posterior prevalence estimate. Our combined posterior prevalence estimate remained stable when varying other prior information. We detected no problematic HMC behaviour, and our posterior predictive checks suggest that our model can reliably recreate our data. Conclusions Accurate population-based estimates of disease are the cornerstone of health service planning and resource allocation. As a greater number of linked population data sources become available, so too does the opportunity for researchers to fully capitalise on the data. The true population prevalence of mood and anxiety disorders may reside between estimates obtained from survey data and health administrative data. We have demonstrated how the use of Bayesian approaches may provide a more informed and accurate estimate of mood and anxiety disorders in the population. This work provides a blueprint for future population-based estimates of disease using linked health data.

Dynamic Monte Carlo Simulation of thel,l-Lactide Ring-Opening Polymerization

2016 ◽  
Vol 11 (1) ◽  
pp. 1600039 ◽  
Author(s):  
Dimitrios Meimaroglou ◽  
Prokopios Pladis ◽  
Costas Kiparissides
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Ring Opening ◽  
Ring Opening Polymerization ◽  
Dynamic Monte Carlo

Dynamic Monte Carlo study on the polymer chain in random media filled with nanoparticles

Polymer ◽  
2006 ◽  
Vol 47 (8) ◽  
pp. 2928-2932 ◽  
Author(s):  
Jianhua Huang ◽  
Zhaofeng Mao ◽  
Changji Qian
Keyword(s):  
Monte Carlo ◽  
Polymer Chain ◽  
Random Media ◽  
Monte Carlo Study ◽  
Dynamic Monte Carlo

Efficient Variance Reduction with Least-Squares Monte Carlo Pricing

2021 ◽  
Author(s):  
François-Michel Boire ◽  
R. Mark Reesor ◽  
Lars Stentoft
Keyword(s):  
Monte Carlo ◽  
Least Squares ◽  
Variance Reduction ◽  
Least Squares Monte Carlo
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