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- W2244802272 abstract "Parameterized optimization and parameter estimation is of great importance in almost every branch of modern science, technology and engineering. A practical issue in the problem is that when the parameter space is large and the available data is noisy, the geometry of the likelihood surface in the parameter space is complicated. This makes searching and optimization algorithms computationally expensive, sometimes even beyond reach. In this chapter, we define a likelihood transform (LT) which can make the structure of the likelihood surface much simpler, hence reducing the intrinsic complexity and easing optimization significantly. We demonstrate the properties of LT by applying it to a toy model in gravitational wave data analysis. For the signal with a signal-to-noise ratio 20, LT has made a deterministic template-based search possible for the first time, which turns out to be much more efficient than an exhaustive grid-based search. The method can also be directly applied to similar matched-filtering-based optimization and parameter estimation problems in other fields." @default.
- W2244802272 created "2016-06-24" @default.
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- W2244802272 date "2015-12-11" @default.
- W2244802272 modified "2023-10-17" @default.
- W2244802272 title "Likelihood Transform: Making Optimization and Parameter Estimation Easier" @default.
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- W2244802272 doi "https://doi.org/10.1007/978-3-319-26389-2_13" @default.
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