On a measure of divergence between two statistical populations defined by their probability distributions

By A. Bhattacharyya. 1943

Subfields: Statistics and Probability, Information Theory

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Citations in the corpus

Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, methodology): “The PPK is in fact closely related to well-known kernels such as the Bhattacharyya kernel (Bhattacharyya 1943) and the exponential symmetrized Kullback-Leibler (KL) divergence (Moreno et al. 2004). In Hein and Bousquet (2005), an extension of a two-parameter family of Hilbertian metrics of Topsøe wa”
Generative and Latent Mean Map Kernels (2010, methodology): “To our knowledge, the Bhattacharyya affinity is the earliest form of a similarity measure between probability distributions [2], whereas the Fisher kernel [9] is the earliest one used as a machine learning method.”
Sensitivity analysis in Bayesian generalized linear mixed models for binary data (2011, background): “The choice of the prior for random-effect precision can be critical when the number of groups is large compared to the number of observations in each group (Box and Tiao 1973, Chapter 7). We face an important practical question: “How sensitive is the posterior distribution of the random effect to ch”
Cramer-Rao Lower Bound and Information Geometry (2013, background): “Although there were already precursor geometric work [35, 12, 36] linking geometry to statistics by the Indian community (Professors Mahalanobis and Bhattacharyya), none of them studied the differential concepts and made the connection with the Fisher information matrix.”
Symmetrizing the Kullback-Leibler Distance (2001, background): “A special case of the Chernoff distance is the Bhattacharyya distance [3, 14] B(p0, p1) = − logμ( (1)2 ).”
Matching 3D models with shape distributions (2001, methodology): “Examples include the Minkowski norms, Kolmogorov-Smirnov distance, Kullback-Leibler divergence distances [37], Match distances [49, 57], Earth Mover’s distance [48], and Bhattacharyya distance [15].”
A review of channel selection algorithms for EEG signal processing (2015, methodology): “This method used Bhattacharyya bound of common spatial patterns [32] as an optimal index and fast forward search to find the optimal combination of channels.”
The Bhattacharyya Measure (2008, background): “kosta@cs.yorku.ca Version 1.0 March 20, 2008”
Probabilistic Embeddings for Cross-Modal Retrieval (2021, background): “When ρ “ 1, it is called the expected likelihood kernel (ELK), and when ρ “ 1{2, it is called Bhattacharyya’s affinity [1], or Bhattacharyya kernel.”
MSMO: Multimodal Summarization with Multimodal Output (2018, methodology): “calculate the similarity between the candidate image and each reference image in three ways: 1) I-I: similarities between the global fc7 features, 2) Hist: Bhattacharyya distance9 for histogram comparison (Bhattacharyya, 1943), and 3) Temp:”
‘Extended spectral angle mapping (ESAM)’ for citrus greening disease detection using airborne hyperspectral imaging (2014, methodology): “Mewes et al. (2011) evaluated the suitability of Bhattacharyya distance (BD) (Bhattacharyya 1943) for feature selection, to identify bands within the feature space for efficient classification.”
Statistical inference for home range overlap (2018, methodology): “While several valid estimators exist, the BC (Bhattacharyya, 1943) stands out because of its statistical validity, geometric interpretability, computational efficiency, and asymptotic consistency.”
Comprehensive Survey on Distance/Similarity Measures between Probability Density Functions (2007, background): “Bhattacharyya distance given in the eqn (33), which is a value between 0 and 1, provides bounds on the Bayes misclassification probability [23].”
Geometry of Quantum States: Index (2007, methodology): “The Fisher–Rao metric was introduced by Rao (1945) [766], based on earlier work by Fisher (1925) [312] and Bhattacharyya (1943) [132].”