Simulation and Monte Carlo
26 Jan 2007-
About: The article was published on 2007-01-26. It has received 85 citations till now. The article focuses on the topics: Dynamic Monte Carlo method & Hybrid Monte Carlo.
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01 Aug 2010
TL;DR: This review paper is to review healthcare simulation literature that have been published between 1970 and 2007 in high-quality journals belonging to various subject categories and that report on the application of four simulation techniques, namely, Monte Carlo simulation, discrete-event simulation, system dynamics and agent-based simulation.
Abstract: The publications that relate to the application of simulation to healthcare have steadily increased over the years. These publications are scattered amongst various journals that belong to several subject categories, including operational research, health economics and pharmacokinetics. The simulation techniques that are applied to the study of healthcare problems are also various. The aim of this study, therefore, is to review healthcare simulation literature that have been published between 1970 and 2007 in high-quality journals belonging to various subject categories and that report on the application of four simulation techniques, namely, Monte Carlo simulation, discrete-event simulation, system dynamics and agent-based simulation. Arguably, journal impact factor is fundamental in assessing the quality of publications. Thus, the 201 publications selected for review have been queried from the ISI Web of Science® bibliographic database of high-impact research journals. Through a review of healthcare simulation literature the following three objectives have been realized: a papers have been categorized under the different simulation techniques, and the healthcare problems that each technique is employed to investigate are identified; b variables such as authors, article citations, etc., within our dataset of healthcare papers have been profiled; c turning point strategically important papers and authors have been identified through co-citation analysis of references cited by the papers in our dataset. The above objectives have been realized by devising and then employing a methodology for profiling literature. It is expected that this review paper will help the readers gain a broader understanding of research in healthcare simulation.
106 citations
Cites methods from "Simulation and Monte Carlo"
...MCS is a statistical technique, with roots in World War II, which uses a sequence of random numbers to generate values from a known probability distribution associated with a source of uncertainty [25]....
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TL;DR: This paper analyzes the performance of Dagpunar’s algorithm and combines it with a new rejection method which ensures a uniformly fast generator and finds it suitable for the varying parameter case.
Abstract: The generalized inverse Gaussian distribution has become quite popular in financial engineering. The most popular random variate generator is due to Dagpunar (Commun. Stat., Simul. Comput. 18:703–710, 1989). It is an acceptance-rejection algorithm method based on the Ratio-of-Uniforms method. However, it is not uniformly fast as it has a prohibitive large rejection constant when the distribution is close to the gamma distribution. Recently some papers have discussed universal methods that are suitable for this distribution. However, these methods require an expensive setup and are therefore not suitable for the varying parameter case which occurs in, e.g., Gibbs sampling. In this paper we analyze the performance of Dagpunar's algorithm and combine it with a new rejection method which ensures a uniformly fast generator. As its setup is rather short it is in particular suitable for the varying parameter case.
61 citations
TL;DR: A short overview of some of the most important Monte Carlo techniques and their applications can be found in this article, with a more persistent presentation of a specific class of Monte Carlo methods that can be implemented to the different scales of a polymerization process in order to predict the dynamic evolution of key properties of the polymer in terms of varying process conditions.
Abstract: Monte Carlo (MC) is a term quite common in the research community but, quite surprisingly, it may possess a different meaning among researchers in different areas. This paradox is derived from the potential of the method to serve as a modeling tool of systems that belong to a very wide range of different areas in science, such as mathematics, biology, economics, and physics. Thus, depending on the nature of the system under study and the type of the calculated properties, a different variation of the MC method may be encountered. In the present work, an attempt is made to provide a short overview of some of the most important MC techniques and their applications. Special emphasis is placed on problems related to the polymer science, with a more persistent presentation of a specific class of MC methods that can be implemented to the different scales of a polymerization process in order to predict the dynamic evolution of key properties of the polymer in terms of the varying process conditions. The latest d...
54 citations
TL;DR: Seven pervasive statistical flaws in intervention designs are presented, each illustrated with a Monte Carlo simulation to present its underlying mechanisms, gauge its magnitude, and discuss potential remedies.
Abstract: The prospect of enhancing cognition is undoubtedly among the most exciting research questions currently bridging psychology, neuroscience, and evidence-based medicine. Yet, convincing claims in this line of work stem from designs that are prone to several shortcomings, thus threatening the credibility of training-induced cognitive enhancement. Here, we present seven pervasive statistical flaws in intervention designs: (i) lack of power; (ii) sampling error; (iii) continuous variable splits; (iv) erroneous interpretations of correlated gain scores; (v) single transfer assessments; (vi) multiple comparisons; and (vii) publication bias. Each flaw is illustrated with a Monte Carlo simulation to present its underlying mechanisms, gauge its magnitude, and discuss potential remedies. Although not restricted to training studies, these flaws are typically exacerbated in such designs, due to ubiquitous practices in data collection or data analysis. The article reviews these practices, so as to avoid common pitfalls when designing or analyzing an intervention. More generally, it is also intended as a reference for anyone interested in evaluating claims of cognitive enhancement.
47 citations
Cites background from "Simulation and Monte Carlo"
...Indeed,much knowledge can be gained by incorporating simulated data to complex research problems (Rubinstein and Kroese, 2011), either because they are difficult to visualize or because the representation of their outcomes is ambiguous....
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03 Jan 2005
TL;DR: Efficiency issues in implementation of Monte Carlo algorithm for 3D capacitance extraction are addressed and error bounds in statistical capacitance estimation are discussed.
Abstract: In this article we address efficiency issues in implementation of Monte Carlo algorithm For 3D capacitance extraction. Error bounds in statistical capacitance estimation are discussed. Methods to tighten them through variance reduction techniques are detailed. Sample values in implementation of Monte Carlo algorithm are completely determined by the first hop in random walk. This in turn facilitates application of variance reduction techniques like importance sampling and stratified sampling to be used effectively. Experimental results indicate average speedup of 16X in simple uniform dielectric technologies, 7.3X in technologies with layers of dielectrics and 4.6X in technologies having conformal dielectrics.
28 citations
Cites background from "Simulation and Monte Carlo"
...A detailed survey of them can be obtained from standard texts on Monte Carlo integration [14], [15], [16], [17]....
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