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179,602
A Bayesian computer vision system for modeling human interactions
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2000
"... We describe a realtime computer vision and machine learning system for modeling and recognizing human behaviors in a visual surveillance task [1]. The system is particularly concerned with detecting when interactions between people occur and classifying the type of interaction. Examples of interes ..."
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Cited by 539 (6 self)
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We describe a realtime computer vision and machine learning system for modeling and recognizing human behaviors in a visual surveillance task [1]. The system is particularly concerned with detecting when interactions between people occur and classifying the type of interaction. Examples
Bayesian computations
, 2016
"... Declarations can be found on page 19 DOI 10.7717/peerj.1910 Copyright 2016 Rougemont et al. ..."
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Declarations can be found on page 19 DOI 10.7717/peerj.1910 Copyright 2016 Rougemont et al.
Bayesian Computation
"... Accelerating inference for diffusions observed with measurement error and large ..."
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Accelerating inference for diffusions observed with measurement error and large
Approximate Bayesian Computational methods
 Statistics and Computing
, 2011
"... Also known as likelihoodfree methods, approximate Bayesian computational (ABC) methods have appeared in the past ten years as the most satisfactory approach to intractable likelihood problems, first in genetics then in a broader spectrum of applications. However, these methods suffer to some degr ..."
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Cited by 65 (7 self)
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Also known as likelihoodfree methods, approximate Bayesian computational (ABC) methods have appeared in the past ten years as the most satisfactory approach to intractable likelihood problems, first in genetics then in a broader spectrum of applications. However, these methods suffer to some
Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
 Biometrika
, 1995
"... Markov chain Monte Carlo methods for Bayesian computation have until recently been restricted to problems where the joint distribution of all variables has a density with respect to some xed standard underlying measure. They have therefore not been available for application to Bayesian model determi ..."
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Cited by 1342 (23 self)
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Markov chain Monte Carlo methods for Bayesian computation have until recently been restricted to problems where the joint distribution of all variables has a density with respect to some xed standard underlying measure. They have therefore not been available for application to Bayesian model
Bayesian Computation: Practical Exercises
, 2005
"... This is a collection of practical exercises from old courses, mostly my old module in “Bayesian Computation”. Unless otherwise stated, references to lecture notes refer to the “Bayesian Computation ” notes. The exercises use R and BUGS. Both R and BUGS are available in the School of Mathematics and ..."
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This is a collection of practical exercises from old courses, mostly my old module in “Bayesian Computation”. Unless otherwise stated, references to lecture notes refer to the “Bayesian Computation ” notes. The exercises use R and BUGS. Both R and BUGS are available in the School of Mathematics
Bayesian Network Classifiers
, 1997
"... Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with stateoftheart classifiers such as C4.5. This fact raises the question of whether a classifier with less restr ..."
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Cited by 793 (20 self)
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Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with stateoftheart classifiers such as C4.5. This fact raises the question of whether a classifier with less
Bayesian Data Analysis
, 1995
"... I actually own a copy of Harold Jeffreys’s Theory of Probability but have only read small bits of it, most recently over a decade ago to confirm that, indeed, Jeffreys was not too proud to use a classical chisquared pvalue when he wanted to check the misfit of a model to data (Gelman, Meng and Ste ..."
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Cited by 2186 (63 self)
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the following: (1) in thinking about prior distributions, we should go beyond Jeffreys’s principles and move toward weakly informative priors; (2) it is natural for those of us who work in social and computational sciences to favor complex models, contra Jeffreys’s preference for simplicity; and (3) a key
Bayesian Computational Tools∗
"... Abstract: This chapter surveys advances in the field of Bayesian computation over the past twenty years, from a purely personnal viewpoint, hence containing some ommissions given the spectrum of the field. Monte Carlo, MCMC and ABC themes are thus covered here, while the rapidly expanding area of p ..."
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Abstract: This chapter surveys advances in the field of Bayesian computation over the past twenty years, from a purely personnal viewpoint, hence containing some ommissions given the spectrum of the field. Monte Carlo, MCMC and ABC themes are thus covered here, while the rapidly expanding area
Results 1  10
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179,602