"Fisher information metric"의 두 판 사이의 차이
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===소스=== | ===소스=== | ||
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+ | ==메타데이터== | ||
+ | ===위키데이터=== | ||
+ | * ID : [https://www.wikidata.org/wiki/Q5454858 Q5454858] | ||
+ | ===Spacy 패턴 목록=== | ||
+ | * [{'LOWER': 'fisher'}, {'LOWER': 'information'}, {'LEMMA': 'metric'}] |
2021년 2월 17일 (수) 01:09 기준 최신판
노트
위키데이터
- ID : Q5454858
말뭉치
- 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]
- I used Fisher information matrix in defining the metric, so it was called Fisher – Rao metric.[1]
- We have developed a unified theory for sustainability based on Fisher information.[2]
- Fisher information tracks dynamic order in a system.[2]
- The Fisher Information analysis was able to identify two regime shifts (one in 1977 and other in 1989) that have been established before.[2]
- The first is the so-called Fisher information which appears in some versions of the log-Sobolev inequality.[3]
- The second is the so called Fisher information metric or Fisher-Rao metric.[3]
- I searched for literature on the Fisher information matrix formation for hierarchical models, but in vain.[4]
- Fisher metric is a metric appearing in information geometry, see there for more information and references.[5]
- The Fisher Information Matrix describes the covariance of the gradient of the log-likelihood function.[6]
- 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]
- To compute , we sample the data from task A once and calculate the empirical Fisher Information Matrix as described before.[6]
- 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]
- Our approach enables a dynamical approach to the Fisher information metric.[7]
- The Fisher metric can be derived from the concept of relative entropy.[8]
- 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]
- It’s called the Fisher metric, at least up to a constant factor that I won’t worry about here.[8]
- They might have been hoping that deforming relative entropy would lead to interestingly deformed versions of the Fisher metric.[8]
- (2015) that characterizes the Fisher metric on finite sample spaces via the monotonicity.[9]
- 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]
- To this end, a metric that integrates the essential elements of the sensor selection problem is defined from the Fisher information matrix.[11]
- Elements of information theory are then introduced in the scope of sensor selection, with a particular focus on the Fisher information matrix (FIM).[11]
- For the kind of static systems described by (6), the Fisher information matrix (FIM) is a mathematical entity that possesses the aforementioned features.[11]
- 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]
- 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]
- Considered purely as a matrix, it is known as the Fisher information matrix.[13]
- The last can be recognized as one-fourth of the Fisher information metric.[13]
- 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]
- The Fisher information matrix is used to calculate the covariance matrices associated with maximum-likelihood estimates.[14]
- 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]
- A random variable carrying high Fisher information implies that the absolute value of the score is often high.[14]
- Thus, the Fisher information may be seen as the curvature of the support curve (the graph of the log-likelihood).[14]
- We use the Fisher information distance which is constructed from the Fisher information metric of the distribution family for this purpose.[15]
- First, we develop a closed form expression for the Fisher information distance between one-dimensional models.[15]
- Next, we compute the components of the Fisher information matrix for the generalized Pareto and generalized extreme value distributions.[15]
소스
- ↑ 1.0 1.1 Fisher-Rao metric
- ↑ 2.0 2.1 2.2 FISHER INFORMATION AS A SUSTAINABILITY METRIC
- ↑ 3.0 3.1 What is the relationship between the Fisher Information and the Fisher Information metric?
- ↑ Fisher information metric for hierarchical Bayesian model is negative-definite?
- ↑ Fisher metric in nLab
- ↑ 6.0 6.1 6.2 6.3 Fisher Information Matrix · Yuan-Hong Liao (Andrew)
- ↑ Dynamics of the Fisher information metric : Sussex Research Online
- ↑ 8.0 8.1 8.2 8.3 The n-Category Café
- ↑ The uniqueness of the Fisher metric as information metric
- ↑ Fisher information metrics for binary classifier evaluation and training
- ↑ 11.0 11.1 11.2 11.3 The Fisher Information Matrix as a Relevant Tool for Sensor Selection in Engine Health Monitoring
- ↑ Geometry of Fisher Information Metric and the Barycenter Map
- ↑ 13.0 13.1 13.2 Fisher information metric
- ↑ 14.0 14.1 14.2 14.3 Fisher information
- ↑ 15.0 15.1 15.2 Clustering Financial Return Distributions Using the Fisher Information Metric
메타데이터
위키데이터
- ID : Q5454858
Spacy 패턴 목록
- [{'LOWER': 'fisher'}, {'LOWER': 'information'}, {'LEMMA': 'metric'}]