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Distribution of Mutual Information from Complete and Incomplete Data [article]

Marcus Hutter, Marco Zaffalon
2004 arXiv   pre-print
This makes the distribution of mutual information become a concrete alternative to descriptive mutual information in many applications which would benefit from moving to the inductive side.  ...  The derived analytical expressions allow the distribution of mutual information to be approximated reliably and quickly.  ...  reliable methods to approximate the distribution of mutual information from either complete or incomplete data.  ... 
arXiv:cs/0403025v1 fatcat:ajorltbcezej7iyvqtptzjqix4

Distribution of mutual information from complete and incomplete data

Marcus Hutter, Marco Zaffalon
2005 Computational Statistics & Data Analysis  
This makes the distribution of mutual information become a concrete alternative to descriptive mutual information in many applications which would benefit from moving to the inductive side.  ...  The derived analytical expressions allow the distribution of mutual information to be approximated reliably and quickly.  ...  reliable methods to approximate the distribution of mutual information from either complete or incomplete data.  ... 
doi:10.1016/j.csda.2004.03.010 fatcat:7rrqqckt7zex7lm5rnmtu5d6wq

Bayesian Treatment of Incomplete Discrete Data Applied to Mutual Information and Feature Selection [chapter]

Marcus Hutter, Marco Zaffalon
2003 Lecture Notes in Computer Science  
A fast filter based on the distribution of mutual information is shown to outperform the traditional filter based on empirical mutual information on a number of incomplete real data sets.  ...  The Bayesian treatment of unknown chances involves computing, from a second order prior distribution and the data likelihood, a posterior distribution of the chances.  ...  Distribution of Mutual Information Mutual information I.  ... 
doi:10.1007/978-3-540-39451-8_29 fatcat:2rzfnw3rhjhdzhdbj2tl2skm6y

Bayesian Treatment of Incomplete Discrete Data applied to Mutual Information and Feature Selection [article]

Marcus Hutter, Marco Zaffalon
2003 arXiv   pre-print
A fast filter based on the distribution of mutual information is shown to outperform the traditional filter based on empirical mutual information on a number of incomplete real data sets.  ...  The Bayesian treatment of unknown chances involves computing, from a second order prior distribution and the data likelihood, a posterior distribution of the chances.  ...  Distribution of Mutual Information Mutual information I.  ... 
arXiv:cs/0306126v1 fatcat:doq3vvh3gvdjlgxu3rjcpfjvfq

Estimating the Extent of Trade Under Incomplete Information: The Case of HIV

Tomas J. Philipson
1997 Social Science Research Network  
This paper discusses problems and prospects in the empirical analysis of the effects of incomplete information on mutually beneficial activities when direct measures on information are available from traders  ...  The paper shows how well the joint distribution of information within a couple can be identified under one-sided sampling by deriving the bounds within which the joint distribution must fall.  ...  of incomplete information.  ... 
doi:10.2139/ssrn.1782 fatcat:m2ouq53szfeippsdrx52jdru2a

Mutual Kernel Matrix Completion

Rachelle RIVERO, Richard LEMENCE, Tsuyoshi KATO
2017 IEICE transactions on information and systems  
With the huge influx of various data nowadays, extracting knowledge from them has become an interesting but tedious task among data scientists, particularly when the data come in heterogeneous form and  ...  However, among the many data completion techniques available in the literature, studies about mutually completing several incomplete kernel matrices have not been given much attention yet.  ...  Most of the time, the data obtained are noisy and have missing information.  ... 
doi:10.1587/transinf.2017edp7059 fatcat:cte4ww77ifbfpikvgdsxmtr4qu

Deep learning of quantum entanglement from incomplete measurements [article]

Dominik Koutný, Laia Ginés, Magdalena Moczała-Dusanowska, Sven Höfling, Christian Schneider, Ana Predojević, Miroslav Ježek
2022 arXiv   pre-print
Finally, we derive a method based on a convolutional network input that can accept data from various measurement scenarios and perform, to some extent, independently of the measurement device.  ...  Our method allows for direct quantification of the quantum correlations using an incomplete set of local measurements.  ...  ACKNOWLEDGMENTS We acknowledge the use of cluster computing resources provided by the Department of Optics, Palacký University Olomouc. We thank J.  ... 
arXiv:2205.01462v2 fatcat:eu2xlpzdvzf2tlmp4ybpxnz6ne

A hybrid imputation approach for microarray missing value estimation

Huihui Li, Changbo Zhao, Fengfeng Shao, Guo-Zheng Li, Xiao Wang
2015 BMC Genomics  
Technically, most existing approaches suffer from this prevalent problem. Imputation is one of the frequently used methods for processing missing data.  ...  Specifically, RMI exploits global correlation information and local structure in the data, captured by two popular methods, Bayesian Principal Component Analysis (BPCA) and Local Least Squares (LLS), respectively  ...  Funding This work and its publication were supported by the Natural Science Foundation of China under grant no. 61273305 and no. 81274007, no.61402422.  ... 
doi:10.1186/1471-2164-16-s9-s1 pmid:26330180 pmcid:PMC4547405 fatcat:q34dsckwrzhyni5zfnkxm6keuq

Mutual Kernel Matrix Completion [article]

Tsuyoshi Kato, Rachelle Rivero
2017 arXiv   pre-print
With the huge influx of various data nowadays, extracting knowledge from them has become an interesting but tedious task among data scientists, particularly when the data come in heterogeneous form and  ...  However, among the many data completion techniques available in the literature, studies about mutually completing several incomplete kernel matrices have not been given much attention yet.  ...  [6] used multiple auxiliary complete matrices to complete a single incomplete matrix. In this study, we mutually complete multiple incomplete matrices.  ... 
arXiv:1702.04077v3 fatcat:bcfnz36u4jc4dpnv5a4skbxwbi

Estimating the success of re-identifications in incomplete datasets using generative models

Luc Rocher, Julien M. Hendrickx, Yves-Alexandre de Montjoye
2019 Nature Communications  
While rich medical, behavioral, and socio-demographic data are key to modern data-driven research, their collection and use raise legitimate privacy concerns.  ...  of the de-identification release-and-forget model.  ...  We acknowledge support from the Information Commissioner Office for the development of the online demonstration tool.  ... 
doi:10.1038/s41467-019-10933-3 pmid:31337762 pmcid:PMC6650473 fatcat:qhltlarzuzexhotrc3phpb72wq

Explaining cooperation in the finitely repeated simultaneous and sequential prisoner's dilemma game under incomplete and complete information

Jacob Dijkstra, Marcel A. L. M. van Assen
2016 The Journal of mathematical sociology  
We therefore build a model of how individuals in a finitely repeated PD with incomplete information about their partner's preference for mutual cooperation decide about cooperation.  ...  Explaining cooperation in the finitely repeated simultaneous and sequential prisoner's dilemma game under incomplete and complete information Dijkstra, Jacob; van Assen, Marcus ABSTRACT Explaining cooperation  ...  The upper-left cell of Table 3 shows the proportions of cooperation and mutual cooperation and the efficiency for the incomplete and complete information versions of the two-shot game of this example  ... 
doi:10.1080/0022250x.2016.1226301 fatcat:hkqjzp6c35b37kcqz64qv2wsfe

Multiple imputation as a missing data machine

J Brand, S van Buuren, E M van Mulligen, T Timmers, E Gelsema
1994 Proceedings. Symposium on Computer Applications in Medical Care  
Multiple imputation is a statistically sound method for handling incomplete data. Application of multiple imputation requires a lot of work and not every user is able to do this.  ...  After signalling some shortcomings of popular solutions to incomplete data problems, we outline the concepts behind multiple imputation.  ...  Problems that are associated with incomplete data are: (1) cases with missing data may differ systematically from complete cases so that the sample is no longer representative. (2) less information is  ... 
pmid:7949939 pmcid:PMC2247969 fatcat:zmysvd42k5eqretw25wqp7pcw4

Cause-Effect Deep Information Bottleneck For Systematically Missing Covariates [article]

Sonali Parbhoo, Mario Wieser, Aleksander Wieczorek, Volker Roth
2020 arXiv   pre-print
, even where data is incomplete.  ...  Estimating the causal effects of an intervention from high-dimensional observational data is difficult due to the presence of confounding.  ...  ) where V 1 and V 2 are low-dimensional discrete representations of the covariate data, Z = (V 1 , V 2 ) is a concatenation of V 1 and V 2 and I represents the mutual information parameterised by networks  ... 
arXiv:1807.02326v3 fatcat:o64pbwr4kbenlbpb3f5xtfrnhq

Diagnose the mild cognitive impairment by constructing Bayesian network with missing data

Yan Sun, Yiyuan Tang, Shuxue Ding, Shipin Lv, Yifen Cui
2011 Expert systems with applications  
In this system, we mainly deal with following problems: (1) Estimate missing data in the experiment by utilizing mutual information and Newton interpolation. (2) Make certain the prior feature ordering  ...  Mild Cognitive Impairment (MCI) is thought to be the prodromal phase to Alzheimer's disease (AD), which is the most common form of dementia and leads to irreversible neurogenerative damage of the brain  ...  The authors thank the members of the department of neurology of Dalian University affiliated Xinhua hospital for their invaluable help with this study.  ... 
doi:10.1016/j.eswa.2010.06.084 fatcat:j33w7tdazrawncmbg3zi6fqsuu

Wasserstein Dependency Measure for Representation Learning [article]

Sherjil Ozair, Corey Lynch, Yoshua Bengio, Aaron van den Oord, Sergey Levine, Pierre Sermanet
2019 arXiv   pre-print
In this work, we empirically demonstrate that mutual information-based representation learning approaches do fail to learn complete representations on a number of designed and real-world tasks.  ...  However, such approaches are fundamentally limited since a tight lower bound of mutual information requires sample size exponential in the mutual information.  ...  Acknowledgement The authors would like to thank Ben Poole, George Tucker, Alex Alemi, Alex Lamb, Aravind Srinivas, and Luke Metz for useful discussions and feedback on our research.  ... 
arXiv:1903.11780v1 fatcat:x27to7vs3fe6zdpxrqtl4p5djm
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