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Neural Estimation of Statistical Divergences
J. Mach. Learn. Res., 2021 Statistical divergences (SDs), which quantify the dissimilarity between probability distributions, are a basic constituent of statistical inference and machine learning. A modern method for estimating those divergences relies on parametrizing an empirical variational form by a neural network (NN) and optimizing over parameter space.
Sreejith Sreekumar, Ziv Goldfeld
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Estimation of species divergence times in presence of cross-species gene flow
Systematic Biology, 2023 Cross-species introgression can have significant impacts on phylogenomic reconstruction of species divergence events. Here, we used simulations to show how the presence of even a small amount of introgression can bias divergence time estimates when gene ...
G. Tiley +8 more
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Diverging Moments and Parameter Estimation [PDF]
Journal of the American Statistical Association, 2005 Heavy-tailed distributions are enjoying increased popularity and are becoming more readily applicable as the arsenal of analytical and numerical tools grows. They play key roles in modeling approaches in networking, finance, and hydrology, to name but a few areas.
Gonçalves, Paulo, Riedi, Rudolf
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Contingency Table Analysis and Inference via Double Index Measures
Entropy, 2022 In this work, we focus on a general family of measures of divergence for estimation and testing with emphasis on conditional independence in cross tabulations.
Christos Meselidis, Alex Karagrigoriou
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exTREEmaTIME: a method for incorporating uncertainty into divergence time estimates
Biology Open, 2022 We present a method of divergence time estimation (exTREEmaTIME) that aims to effectively account for uncertainty in divergence time estimates. The method requires a minimal set of assumptions, and, based on these assumptions, estimates the oldest ...
Tom Carruthers, Robert W. Scotland
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F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits
Entropy, 2021 Generative modelling is an important unsupervised task in machine learning. In this work, we study a hybrid quantum-classical approach to this task, based on the use of a quantum circuit born machine. In particular, we consider training a quantum circuit
Chiara Leadbeater +3 more
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Robust Regression with Density Power Divergence: Theory, Comparisons, and Data Analysis
Entropy, 2020 Minimum density power divergence estimation provides a general framework for robust statistics, depending on a parameter α , which determines the robustness properties of the method. The usual estimation method is numerical minimization of the power
Marco Riani +3 more
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Operational meanings of a generalized conditional expectation in quantum metrology [PDF]
Quantum, 2023 A unifying formalism of generalized conditional expectations (GCEs) for quantum mechanics has recently emerged, but its physical implications regarding the retrodiction of a quantum observable remain controversial.
Mankei Tsang
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pixy: Unbiased estimation of nucleotide diversity and divergence in the presence of missing data
bioRxiv, 2020 Population genetic analyses often use summary statistics to describe patterns of genetic variation and provide insight into evolutionary processes. Among the most fundamental of these summary statistics are π and dXY, which are used to describe genetic ...
Katharine L Korunes, K. Samuk
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Amplifying Inter-Message Distance: On Information Divergence Measures in Big Data
IEEE Access, 2017 Message identification (M-I) divergence is an important measure of the information distance between probability distributions, similar to Kullback-Leibler (K-L) and Renyi divergence. In fact, M-I divergence with a variable parameter can make an effect on
Rui She, Shanyun Liu, Pingyi Fan
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