Results 1  10
of
417,206
Sensitivity analysis for finite Markov chains in discrete time
"... When the initial and transition probabilities of a finite Markov chain in discrete time are not well known, we should perform a sensitivity analysis. This is done by considering as basic uncertainty models the socalled credal sets that these probabilities are known or believed to belong to, and by ..."
Abstract

Cited by 4 (1 self)
 Add to MetaCart
When the initial and transition probabilities of a finite Markov chain in discrete time are not well known, we should perform a sensitivity analysis. This is done by considering as basic uncertainty models the socalled credal sets that these probabilities are known or believed to belong to
Exact Sampling with Coupled Markov Chains and Applications to Statistical Mechanics
, 1996
"... For many applications it is useful to sample from a finite set of objects in accordance with some particular distribution. One approach is to run an ergodic (i.e., irreducible aperiodic) Markov chain whose stationary distribution is the desired distribution on this set; after the Markov chain has ..."
Abstract

Cited by 548 (13 self)
 Add to MetaCart
For many applications it is useful to sample from a finite set of objects in accordance with some particular distribution. One approach is to run an ergodic (i.e., irreducible aperiodic) Markov chain whose stationary distribution is the desired distribution on this set; after the Markov chain
Finite state Markovchain approximations to univariate and vector autoregressions
 Economics Letters
, 1986
"... The paper develops a procedure for finding a discretevalued Markov chain whose sample paths approximate well those of a vector autoregression. The procedure has applications in those areas of economics, finance, and econometrics where approximate solutions to integral equations are required. 1. ..."
Abstract

Cited by 472 (0 self)
 Add to MetaCart
The paper develops a procedure for finding a discretevalued Markov chain whose sample paths approximate well those of a vector autoregression. The procedure has applications in those areas of economics, finance, and econometrics where approximate solutions to integral equations are required. 1.
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 1330 (24 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
Coupled hidden Markov models for complex action recognition
, 1996
"... We present algorithms for coupling and training hidden Markov models (HMMs) to model interacting processes, and demonstrate their superiority to conventional HMMs in a vision task classifying twohanded actions. HMMs are perhaps the most successful framework in perceptual computing for modeling and ..."
Abstract

Cited by 497 (22 self)
 Add to MetaCart
We present algorithms for coupling and training hidden Markov models (HMMs) to model interacting processes, and demonstrate their superiority to conventional HMMs in a vision task classifying twohanded actions. HMMs are perhaps the most successful framework in perceptual computing for modeling
Sensitivity analysis
, 2000
"... 6359. Authors are listed in alphabetical order. We thank Yuanfang Lin for setting up the data in usable form for our empirical analyses. We thank Prof. Glenn MacDonald and Prof. Mark Daskin for their valuable guidance and comments during the preliminary stages of this project. We appreciate the many ..."
Abstract

Cited by 480 (11 self)
 Add to MetaCart
6359. Authors are listed in alphabetical order. We thank Yuanfang Lin for setting up the data in usable form for our empirical analyses. We thank Prof. Glenn MacDonald and Prof. Mark Daskin for their valuable guidance and comments during the preliminary stages of this project. We appreciate the many insightful comments by
Maximum entropy markov models for information extraction and segmentation
, 2000
"... Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many textrelated tasks, such as partofspeech tagging, text segmentation and information extraction. In these cases, the observations are usually modeled as multinomial ..."
Abstract

Cited by 554 (18 self)
 Add to MetaCart
Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many textrelated tasks, such as partofspeech tagging, text segmentation and information extraction. In these cases, the observations are usually modeled
Segmentation of brain MR images through a hidden Markov random field model and the expectationmaximization algorithm
 IEEE TRANSACTIONS ON MEDICAL. IMAGING
, 2001
"... The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogrambased model, the FM has an intrinsic limi ..."
Abstract

Cited by 619 (14 self)
 Add to MetaCart
The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogrambased model, the FM has an intrinsic
A theory of timed automata
, 1999
"... Model checking is emerging as a practical tool for automated debugging of complex reactive systems such as embedded controllers and network protocols (see [23] for a survey). Traditional techniques for model checking do not admit an explicit modeling of time, and are thus, unsuitable for analysis of ..."
Abstract

Cited by 2651 (32 self)
 Add to MetaCart
using finitely many realvalued clock variables. Automated analysis of timed automata relies on the construction of a finite quotient of the infinite space of clock valuations. Over the years, the formalism has been extensively studied leading to many results establishing connections to circuits
Results 1  10
of
417,206