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Variational bayes for modeling score distributions

Keshi Dai, Evangelos Kanoulas, Virgil Pavlu, Javed A. Aslam
2010 Information retrieval (Boston)  
Empirical modeling of the score distributions associated with retrieved documents is an essential task for many retrieval applications.  ...  Applying Variational Bayes we automatically trade-off the goodness-of-fit with the complexity of the model.  ...  Acknowledgments We would like to thank Avi Arampatzis, Jaap Kamps and Stephen Robertson for many useful discussions.  ... 
doi:10.1007/s10791-010-9156-2 fatcat:3sg2i3ggbbbkrpyjiigk2joagi

Sparse Gaussian Markov Random Field Mixtures for Anomaly Detection

Tsuyoshi Ide, Ankush Khandelwal, Jayant Kalagnanam
2016 2016 IEEE 16th International Conference on Data Mining (ICDM)  
model … removed Surviving models with adjusted model parameters time anomaly scoring model Leveraging variational Bayes method for inference Assumption: posterior distribution is factorized  ...   Derived variational Bayes iterative equations based on the Gauss- delta posterior model Thank you!  ... 
doi:10.1109/icdm.2016.0119 dblp:conf/icdm/IdeKK16 fatcat:2cydcu5rnnaw3l5v2qrdtz7jga

Fusion of Large-Scale Genomic Knowledge and Frequency Data Computationally Prioritizes Variants in Epilepsy

Ian M. Campbell, Mitchell Rao, Sean D. Arredondo, Seema R. Lalani, Zhilian Xia, Sung-Hae L. Kang, Weimin Bi, Amy M. Breman, Janice L. Smith, Carlos A. Bacino, Arthur L. Beaudet, Ankita Patel (+6 others)
2013 PLoS Genetics  
Analysis determined Bayes factors and posterior distributions for each gene.  ...  Genes deleted in our subjects with epilepsy had significantly higher pathogenicity scores and Bayes factors compared to subjects referred for non-neurologic indications.  ...  Acknowledgments The authors wish to thank Edward Chen for computational support. Author Contributions  ... 
doi:10.1371/journal.pgen.1003797 pmid:24086149 pmcid:PMC3784560 fatcat:hgr3rprub5fpdbtmzls657ujr4

Temporally-aware algorithms for document classification

Thiago Salles, Leonardo Rocha, Gisele L. Pappa, Fernando Mourão, Wagner Meira, Marcos Gonçalves
2010 Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval - SIGIR '10  
We then extend three ADC algorithms (namely kNN, Rocchio and Naïve Bayes) to incorporate the TWF.  ...  It usually employs a supervised learning strategy, where we first build a classification model using pre-classified documents and then use this model to classify unseen documents.  ...  Similarly, KNN with TWF on scores achieves the best results among all KNN variations, with gains of +8.85% and +3.80% for MacroF1 and Accuracy in the ACM-DL collection.  ... 
doi:10.1145/1835449.1835502 dblp:conf/sigir/SallesRPMMG10 fatcat:xqpgopj2dff5zpvujzyqqga75y

Gait Verification Using Probabilistic Methods

A.I. Bazin, M.S. Nixon
2005 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1  
These likelihoods are combined using Bayes rule and thresholded to provide a decision boundary.  ...  In this paper we describe a novel method for gait based identity verification based on Bayesian classification.  ...  The authors also thank David Wagg for his assistance in processing the gait sequences.  ... 
doi:10.1109/acvmot.2005.55 dblp:conf/wacv/BazinN05 fatcat:naq2q4lkrza2ph4qtkyszva5xm

Unnormalized Variational Bayes [article]

Saeed Saremi
2020 arXiv   pre-print
We unify empirical Bayes and variational Bayes for approximating unnormalized densities.  ...  This framework, named unnormalized variational Bayes (UVB), is based on formulating a latent variable model for the random variable Y=X+N(0,σ^2 I_d) and using the evidence lower bound (ELBO), computed  ...  Acknowledgement I am grateful to Aapo Hyvärinen and Francis Bach for their comments on the manuscript and to my colleagues Christian Osendorfer and Rupesh Srivastava for discussions.  ... 
arXiv:2007.15130v1 fatcat:wgcvwq73ere2bffrizahkfbd2q

Theoretical performance of genetic pattern classifier

S Bandyopadhyay, C.A Murthy, Sankar K Pal
1999 Journal of the Franklin Institute  
It is also shown experimentally that the variation of recognition score with a priori class probability for both the classifiers is similar.  ...  It is shown that the optimum number of hyperplanes generated by the proposed classifier is equal to that required to model the Bayes decision boundary when there exists only one partition of the feature  ...  Variation of ("overall GA score-Bayes score) with n for Data set 1. performance gradually decreases for larger values of n.  ... 
doi:10.1016/s0016-0032(98)00035-0 fatcat:k37ory7sfrejdfmc7ne2ktxysq


Yang Yang
2006 Sociological methodology  
Avenues for future research on mixed APC models are discussed.  ...  This study applies methods of Bayesian statistical inference to hierarchical APC models for the age-period-cohort analysis of repeated cross-section survey data.  ...  b AIC for REML and DIC for Bayes.  ... 
doi:10.1111/j.1467-9531.2006.00003.x fatcat:c43o4vza7rbg7psrbridzhlmxe

Page 7019 of Mathematical Reviews Vol. , Issue 97K [page]

1997 Mathematical Reviews  
Summary: “Berger and Pericchi (1993) proposed a general auto- matic Bayesian method for comparing models, the intrinsic Bayes factor (IBF).  ...  (Y V-SBOL; Caracas) The intrinsic Bayes factor for linear models. (English summary) Bayesian statistics, 5 ( Alicante, 1994), 25-44, Oxford Sci. Publ., Oxford Univ. Press, New York, 1996.  ... 

Variational Hamiltonian Monte Carlo via Score Matching [article]

Cheng Zhang, Babak Shahbaba, Hongkai Zhao
2017 arXiv   pre-print
simulation of Hamiltonian flow, which is the bottleneck for many applications of HMC in big data problems.  ...  The surrogate provides sufficiently accurate approximation while allowing for fast exploration of parameter space, resulting in an efficient approximate inference algorithm.  ...  , for our free-form variational Bayes.  ... 
arXiv:1602.02219v2 fatcat:7k2typdjqfh65l6qs7hpxnqouy


Henry I. Braun, Douglas H. Jones
1984 ETS Research Report Series  
Linda DeLauro and Vera House for assistance in the preparation of this report.  ...  Under model EBFqe, for a given set of predictor scores, the predicted FYA for females tended to be higher than that for males, while the predicted FYA for those over 25 tended to be higher than that for  ...  That is, the differences between the least squares prediction equations for the two departments are largely attributed to random variation and are eliminated by the empirical Bayes process.  ... 
doi:10.1002/j.2330-8516.1984.tb00074.x fatcat:d2oawoppsvg4hfuejk66hzyii4

Modeling motor learning using heteroskedastic functional principal components analysis

Daniel Backenroth, Jeff Goldsmith, Michelle D. Harran, Juan C. Cortes, John W. Krakauer, Tomoko Kitago
2017 Journal of the American Statistical Association  
We extend the functional principal components analysis framework by modeling the variance of principal component scores as a function of covariates and subject-specific random effects.  ...  In a setting where principal components are largely invariant across subjects and covariate values, modeling the variance of these scores provides a flexible and interpretable way to explore factors that  ...  First, we estimate scores ξ ijk using our variational Bayes method, with a constant score variance for each FPC.  ... 
doi:10.1080/01621459.2017.1379403 pmid:30416231 pmcid:PMC6223649 fatcat:h6kkd4v2bjav5n3ik7kyrqyzgu

Estimation of the Number of Speakers with Variational Bayesian PLDA in the DIHARD Diarization Challenge

Ignacio Viñals, Pablo Gimeno, Alfonso Ortega, Antonio Miguel, Eduardo Lleida
2018 Interspeech 2018  
This model, a generative model with latent variables as speaker labels, produces the diarization labels by means of Variational Bayes iterations.  ...  Our proposal for the challenge is a system based on the ivector PLDA paradigm: Given some initial segmentation of the input audio we extract i-vector representations for each acoustic fragment.  ...  The high complexity of the proposed model makes its maximum likelihood solution unfeasible, so a Variational Bayes solution is proposed.  ... 
doi:10.21437/interspeech.2018-1841 dblp:conf/interspeech/VinalsGOML18 fatcat:465yndbim5ekhgpkuujfmxqnwq

Focused Bayesian Prediction [article]

Ruben Loaiza-Maya, Gael M. Martin, David T. Frazier
2020 arXiv   pre-print
A prior is defined over a class of plausible predictive models.  ...  We propose a new method for conducting Bayesian prediction that delivers accurate predictions without correctly specifying the unknown true data generating process.  ...  predictive distributions that yield high scores.  ... 
arXiv:1912.12571v2 fatcat:4uwgsms6cvdmzppd34ekfsoymy

Page 82 of Geographical Analysis Vol. 24, Issue 1 [page]

1992 Geographical Analysis  
Under appropriate regularity conditions (for example, Mardia and Marshall 1984), (B’ ,8’)’ is approximately distributed as a (p+k)-variate Gaussian random vector with mean (B’,@’)’ and variance matrix,  ...  The scoring (or Gauss-Newton) algorithm yields the following iterative scheme (for example, Mardia and Marshall 1984): B® = (x'S(0)-1X)-1x’'50) “IY (25) e+) = 9 — (BO) “L®, (26) where the ith element of  ... 
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