Results 1  10
of
247,374
Maximum likelihood from incomplete data via the EM algorithm
 JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B
, 1977
"... A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situat ..."
Abstract

Cited by 11972 (17 self)
 Add to MetaCart
A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value
A modular threedimensional finitedifference groundwater flow model
 U.S. Geological Survey Techniques of WaterResources Investigations Book 6, Chapter A1
, 1988
"... The primary objective of this course is to discuss the principals of finite difference methods and their applications in groundwater modeling. The emphasis of the class lectures is on the theoretical aspects of numerical modeling (finite difference method). Steps involved in simulation of groundwate ..."
Abstract

Cited by 508 (5 self)
 Add to MetaCart
of groundwater systems under various initial/boundary conditions and management schemes will be practiced. The emphasis of the student presentations will be based on published papers concerning the applied aspects of groundwater computer modeling utilizing finite difference and analytical computer models
Evolutionary Computing
, 2005
"... Evolutionary computing (EC) is an exciting development in Computer Science. It amounts to building, applying and studying algorithms based on the Darwinian principles of natural selection. In this paper we briefly introduce the main concepts behind evolutionary computing. We present the main compone ..."
Abstract

Cited by 624 (35 self)
 Add to MetaCart
Evolutionary computing (EC) is an exciting development in Computer Science. It amounts to building, applying and studying algorithms based on the Darwinian principles of natural selection. In this paper we briefly introduce the main concepts behind evolutionary computing. We present the main
Affective Computing
, 1995
"... Recent neurological studies indicate that the role of emotion in human cognition is essential; emotions are not a luxury. Instead, emotions play a critical role in rational decisionmaking, in perception, in human interaction, and in human intelligence. These facts, combined with abilities computers ..."
Abstract

Cited by 1909 (43 self)
 Add to MetaCart
computers are acquiring in expressing and recognizing affect, open new areas for research. This paper defines key issues in "affective computing," computing that relates to, arises from, or deliberately influences emotions. New models are suggested for computer recognition of human emotion
Markov Random Field Models in Computer Vision
, 1994
"... . A variety of computer vision problems can be optimally posed as Bayesian labeling in which the solution of a problem is defined as the maximum a posteriori (MAP) probability estimate of the true labeling. The posterior probability is usually derived from a prior model and a likelihood model. The l ..."
Abstract

Cited by 516 (18 self)
 Add to MetaCart
. A variety of computer vision problems can be optimally posed as Bayesian labeling in which the solution of a problem is defined as the maximum a posteriori (MAP) probability estimate of the true labeling. The posterior probability is usually derived from a prior model and a likelihood model
Hidden Markov models in computational biology: applications to protein modeling
 JOURNAL OF MOLECULAR BIOLOGY
, 1994
"... Hidden.Markov Models (HMMs) are applied t.0 the problems of statistical modeling, database searching and multiple sequence alignment of protein families and protein domains. These methods are demonstrated the on globin family, the protein kinase catalytic domain, and the EFhand calcium binding moti ..."
Abstract

Cited by 655 (39 self)
 Add to MetaCart
Hidden.Markov Models (HMMs) are applied t.0 the problems of statistical modeling, database searching and multiple sequence alignment of protein families and protein domains. These methods are demonstrated the on globin family, the protein kinase catalytic domain, and the EFhand calcium binding
Computational LambdaCalculus and Monads
, 1988
"... The λcalculus is considered an useful mathematical tool in the study of programming languages, since programs can be identified with λterms. However, if one goes further and uses fijconversion to prove equivalence of programs, then a gross simplification is introduced, that may jeopardise the ap ..."
Abstract

Cited by 501 (6 self)
 Add to MetaCart
the applicability of theoretical results to real situations. In this paper we introduce a new calculus based on a categorical semantics for computations. This calculus provides a correct basis for proving equivalence of programs, independent from any specific computational model.
LogP: Towards a Realistic Model of Parallel Computation
, 1993
"... A vast body of theoretical research has focused either on overly simplistic models of parallel computation, notably the PRAM, or overly specific models that have few representatives in the real world. Both kinds of models encourage exploitation of formal loopholes, rather than rewarding developme ..."
Abstract

Cited by 560 (15 self)
 Add to MetaCart
A vast body of theoretical research has focused either on overly simplistic models of parallel computation, notably the PRAM, or overly specific models that have few representatives in the real world. Both kinds of models encourage exploitation of formal loopholes, rather than rewarding
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 ..."
Abstract

Cited by 538 (6 self)
 Add to MetaCart
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
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 ..."
Abstract

Cited by 1345 (23 self)
 Add to MetaCart
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
Results 1  10
of
247,374