Linked Data Explorerhttp://worldcat.org/entity/work/id/63262376
Efficient approximation in decision models using Monte Carlo simulation
I use the resulting decision model to evaluate the performance and scalability of the algorithms described in this work. The extreme size and complexity of the scQMR- scDT model required the further development of adaptive strategies to better guide the expected value of computation decision making process. I demonstrate that the methods introduced in this work allows approximate solutions for a very large decision model in cases are not amenable to exact computation by any known algorithms.
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http://schema.org/about
- http://experiment.worldcat.org/entity/work/data/63262376#Topic/computer_science
- http://experiment.worldcat.org/entity/work/data/63262376#Topic/algorithms
- http://experiment.worldcat.org/entity/work/data/63262376#Topic/decision_support_techniques
- http://experiment.worldcat.org/entity/work/data/63262376#Topic/health_sciences_general
- http://experiment.worldcat.org/entity/work/data/63262376#Topic/research_design
- http://experiment.worldcat.org/entity/work/data/63262376#Topic/decision_making_computer_assisted
- http://experiment.worldcat.org/entity/work/data/63262376#Topic/computer_simulation
- http://experiment.worldcat.org/entity/work/data/63262376#Topic/monte_carlo_method
http://schema.org/description
- "I use the resulting decision model to evaluate the performance and scalability of the algorithms described in this work. The extreme size and complexity of the scQMR- scDT model required the further development of adaptive strategies to better guide the expected value of computation decision making process. I demonstrate that the methods introduced in this work allows approximate solutions for a very large decision model in cases are not amenable to exact computation by any known algorithms."@en
- "In this dissertation, I develop an approach to solving complex decision-theoretic models using Monte Carlo simulation techniques. I take advantage of recent results in optimal stopping rules for Monte Carlo simulation to develop methods that can estimate the expected utility of decisions considered by the user of a normative system, and the expected value of information of potential tests. These methods do not require the user to know about the Monte Carlo process; the stopping rule theory is used to translate uncertainty in the estimates into terms that have meaning for the user of a system, such as the risk in acting now versus continued solution refinement. Complexity is a problem for many real-world systems, especially in the context of medical decision making."@en
- "Reducing complexity by model simplification will yield unpredictable errors in solutions. The approach taken in this dissertation provides approximate answers to decision problems with a precise bound in the error of the estimated solution in terms of utility. I have developed strategies that improve the efficiency of the simulation process by selectively focusing computational resources on distinguishing among candidates for maximum expected utility actions. I evaluate the methods using a very large medical belief network, the Quick Medical Reference-Decision Theoretic (scQMR- scDT) model. This model is one of the largest probabilistic models available. I describe a simple utility model for a medical triage decisions that I have built for the scQMR- scDT network."@en
http://schema.org/name
- "Efficient approximation in decision models using Monte Carlo simulation"@en