Monte Carlo Method
The Chronic Kidney Disease Model: A General Purpose Model of Disease ...
Chronic kidney disease (CKD) is the focus of recent national policy efforts; however, decision makers must account for multiple therapeutic options, comorbidities and complications. The objective of the Chronic Kidney Disease model is to provide guidance to decision makers.We describe this model and give an example of how it can inform clinical and policy decisions.
Methods: Monte Carlo simulation of CKD natural history and treatment. Health states include myocardial infarction, stroke with and without disability, congestive heart failure, CKD stages 1-5, bone disease, dialysis, transplant and death.
Each cycle is 1 month. Projections account for race, age, gender, diabetes, proteinuria, hypertension, cardiac disease, and CKD stage.
Treatment strategies include hypertension control, diabetes control, use of HMG-CoA reductase inhibitors, use of angiotensin converting enzyme inhibitors, nephrology specialty care, CKD screening, and a combination of these. The model architecture is flexible permitting updates as new data become available.
The primary outcome is quality adjusted life years (QALYs). Secondary outcomes include health state events and CKD progression rate.
Results: The model was validated for GFR change/year -3.0+/-1.9 vs.
-1.7+/-3.4 (in the AASK trial), and annual myocardial infarction and mortality rates 3.6 +/- 0.9% and 1.6 +/- 0.5% vs. 4.4% and 1.6% in the Go study.
To illustrate the model's utility we estimated lifetime impact of a hypothetical treatment for primary prevention of vascular disease. As vascular risk declined, QALY improved but risk of dialysis increased.
At baseline, 20% and 60% reduction: QALYs=17.6, 18.2, and 19.0 and dialysis=7.7%, 8.1%, and 10.4%, respectively.
Conclusions: The CKD Model is a valid, general purpose model intended as a resource to inform clinical and policy decisions improving CKD care. Its value as a tool is illustrated in our example which projects a relationship between decreasing cardiac disease and increasing ESRD.
Monte Carlo Method - News
Methods: Monte Carlo simulation of CKD natural history and treatment. Health states include myocardial infarction, stroke with and without disability, congestive heart failure, CKD stages 1-5, bone disease, dialysis, transplant and death.
June 8 -- Xcelerit today announces the world's fastest execution of a Monte-Carlo option pricing algorithm (Black-Scholes model) on a single unit rack-mounted system. The benchmark was carried out on a new compact 1U Supermicro 6016GT-TF-FM209 GPU
The stochastic method followed a Monte-Carlo simulation concept, ie drivers were classified into so-called correlation groups. For each simulation run, each of the correlation groups took on a best/middle/worst scenario, such that the permutation of
The range is based on a Monte Carlo simulation for accident years 2009 and prior, calibrated to the December 31, 2009 statements. The 90th percentile range for 2010 emergence was from USD22 billion favorable to USD9 billion adverse emergence,
In all, the authors present a host of applications – many of which go beyond standard Excel or VBA usages, for example, how to estimate logit models with maximum likelihood, or how to quickly conduct large-scale Monte Carlo simulations.
Xcelerit puts Monte-Carlo simulations on Steroids with new ...
Demonstration of the World's fastest Monte-Carlo Option Pricing Algorithm
Xcelerit today announces the world's fastest execution of a Monte-Carlo option pricing algorithm (Black-Scholes model) on a single unit rack-mounted system.
The benchmark was carried out on a new compact 1U Supermicro 6016GT-TF-FM209 GPU SuperServer equipped with two brand-new NVIDIA® Tesla™ M2090 GPUs driven to the max using Xcelerit's software development kit.
Monte-Carlo methods are used extensively by the financial engineering community in situations where conventional analytical formulae are impractical or do not exist. The method involves repeated random sampling to generate the results and is a voracious consumer of compute power.
For situations like this, Supermicro, a leading supplier of high-performance, high-efficiency server technology can supply highly concentrated compute capabilities in the form of their GPU SuperServer family. Chris Butler, Enterprise Sales Manager for Supermicro in the UK commented on today's result: "We were delighted to be involved in today's demonstration which really gives our 6016GT server a good work-out."
The Supermicro 6016GT server simply bristles with advanced compute capability. In the driving seat are two Intel® Xeon® X5670 processors. What really impacts on the computation though are the two NVIDIA Tesla M2090 cards slotted into the SuperServer chassis. The GPUs on these cards each can achieve up to 1.3TFLOPS in single precision mode and can be used to split up the computation over several hundreds of thousands of threads.
Simply making all this compute power available is not enough though as Hicham Lahlou, CEO of Xcelerit notes. "Splitting up a complex problem so that it can be executed in parallel across all of these CPUs and GPUs is a task that programmers do not find easy, but our Xcelerit SDK really simplifies this." In addition, it is both difficult and time-consuming to develop separate code-bases to take advantage of each different platform technology that comes along and Xcelerit addresses this problem. "The beauty of our SDK is that users need only one codebase and the tools provided will ensure that it runs seamlessly on many platforms," comments Xcelerit's Lahlou. "These include multi-core CPUs, GPUs, and combinations of these in a cluster."
Investment banks can directly benefit from this blindingly fast option pricing computation. Results can be known earlier, giving a competitive edge to the bank over its competitors when making decisions.
Applications of the Monte Carlo Method in Statistical Physics (Topics in Current Physics): 
Simulation and the Monte Carlo Method (Wiley Series in Probability and Statistics): 
Applications of the Monte Carlo Method in Statistical Physics (Topics in Current Physics): 
Monte Carlo Method for Solving Inverse Problems of Radiation Transfer (Inverse and Ill-Posed Problems): 
The Monte Carlo Method in Condensed Matter Physics (Topics in Applied Physics): Monte Carlo Method - Bookshelf
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Monte Carlo method - Wikipedia, the free encyclopedia
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Monte Carlo method: Definition from Answers.com
Monte Carlo method ( ′mäntē ′kärlō ′methəd ) ( statistics ) A technique which obtains a probabilistic approximation to the solution of a problem by
Monte Carlo methods in finance - Wikipedia, the free encyclopedia
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Monte Carlo Method
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Monte Carlo method
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