"Fisher information metric"의 두 판 사이의 차이

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* ID :  [https://www.wikidata.org/wiki/Q5454858 Q5454858]
 
* ID :  [https://www.wikidata.org/wiki/Q5454858 Q5454858]
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===Spacy 패턴 목록===
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* [{'LOWER': 'fisher'}, {'LOWER': 'information'}, {'LEMMA': 'metric'}]

2021년 2월 17일 (수) 01:09 기준 최신판

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  1. The geodesic distance between two probability distributions induced by the metric (1), with Levi Civita connection associated with Fisher information matrix is defined as the Rao distance.[1]
  2. I used Fisher information matrix in defining the metric, so it was called Fisher – Rao metric.[1]
  3. We have developed a unified theory for sustainability based on Fisher information.[2]
  4. Fisher information tracks dynamic order in a system.[2]
  5. The Fisher Information analysis was able to identify two regime shifts (one in 1977 and other in 1989) that have been established before.[2]
  6. The first is the so-called Fisher information which appears in some versions of the log-Sobolev inequality.[3]
  7. The second is the so called Fisher information metric or Fisher-Rao metric.[3]
  8. I searched for literature on the Fisher information matrix formation for hierarchical models, but in vain.[4]
  9. Fisher metric is a metric appearing in information geometry, see there for more information and references.[5]
  10. The Fisher Information Matrix describes the covariance of the gradient of the log-likelihood function.[6]
  11. Here, we want to use the diagonal components in Fisher Information Matrix to identify which parameters are more important to task A and apply higher weights to them.[6]
  12. To compute , we sample the data from task A once and calculate the empirical Fisher Information Matrix as described before.[6]
  13. With the conclusion above, we can move on to this interesting property: Fisher Information Matrix defines the local curvature in distribution space for which KL-divergence is the metric.[6]
  14. Our approach enables a dynamical approach to the Fisher information metric.[7]
  15. The Fisher metric can be derived from the concept of relative entropy.[8]
  16. But relative entropy can be deformed in various ways, and you might imagine that when you deform it, the Fisher metric gets deformed too.[8]
  17. It’s called the Fisher metric, at least up to a constant factor that I won’t worry about here.[8]
  18. They might have been hoping that deforming relative entropy would lead to interestingly deformed versions of the Fisher metric.[8]
  19. (2015) that characterizes the Fisher metric on finite sample spaces via the monotonicity.[9]
  20. Finally, the expected Fisher information gain from completely random branch splits in the decision tree and its possible relevance in reducing overtraining is analysed.[10]
  21. To this end, a metric that integrates the essential elements of the sensor selection problem is defined from the Fisher information matrix.[11]
  22. Elements of information theory are then introduced in the scope of sensor selection, with a particular focus on the Fisher information matrix (FIM).[11]
  23. For the kind of static systems described by (6), the Fisher information matrix (FIM) is a mathematical entity that possesses the aforementioned features.[11]
  24. A solution could consist in computing the metric from a weighted sum of Fisher information matrices derived for various conditions (both operating and health).[11]
  25. Using Fisher metric and thus obtained properties of geodesics, a fibre space structure of barycenter map and geodesical properties of each fibre are discussed.[12]
  26. Considered purely as a matrix, it is known as the Fisher information matrix.[13]
  27. The last can be recognized as one-fourth of the Fisher information metric.[13]
  28. Again, the first term can be clearly seen to be (one fourth of) the Fisher information metric, by setting α = 0 {\displaystyle \alpha =0} .[13]
  29. The Fisher information matrix is used to calculate the covariance matrices associated with maximum-likelihood estimates.[14]
  30. The Fisher information is a way of measuring the amount of information that an observable random variable X carries about an unknown parameter θ upon which the probability of X depends.[14]
  31. A random variable carrying high Fisher information implies that the absolute value of the score is often high.[14]
  32. Thus, the Fisher information may be seen as the curvature of the support curve (the graph of the log-likelihood).[14]
  33. We use the Fisher information distance which is constructed from the Fisher information metric of the distribution family for this purpose.[15]
  34. First, we develop a closed form expression for the Fisher information distance between one-dimensional models.[15]
  35. Next, we compute the components of the Fisher information matrix for the generalized Pareto and generalized extreme value distributions.[15]

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Spacy 패턴 목록

  • [{'LOWER': 'fisher'}, {'LOWER': 'information'}, {'LEMMA': 'metric'}]