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An introduction to hidden Markov models

by L. R. Rabiner, B. H. Juang - IEEE ASSp Magazine , 1986
"... The basic theory of Markov chains has been known to ..."
Abstract - Cited by 1132 (2 self) - Add to MetaCart
The basic theory of Markov chains has been known to

Basic models in epidemiology

by Fred Brauer, Carlos Castillo-chavez, Fred Brauert, Carlos Castillo-chavez , 1994
"... Basic models in epidemiology ..."
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Basic models in epidemiology

Option Pricing: A Simplified Approach

by John C. Cox, Stephen A. Ross, Mark Rubinstein - Journal of Financial Economics , 1979
"... This paper presents a simple discrete-time model for valumg optlons. The fundamental econonuc principles of option pricing by arbitrage methods are particularly clear In this setting. Its development requires only elementary mathematics, yet it contains as a special limiting case the celebrated Blac ..."
Abstract - Cited by 1016 (10 self) - Add to MetaCart
Black-&holes model, which has previously been derived only by much more difficult methods. The basic model readily lends itself to generalization in many ways. Moreover, by its very constructlon, it gives rise to a simple and efficient numerical procedure for valumg optlons for which premature

Learning in graphical models

by Michael I. Jordan - STATISTICAL SCIENCE , 2004
"... Statistical applications in fields such as bioinformatics, information retrieval, speech processing, image processing and communications often involve large-scale models in which thousands or millions of random variables are linked in complex ways. Graphical models provide a general methodology for ..."
Abstract - Cited by 806 (10 self) - Add to MetaCart
for approaching these problems, and indeed many of the models developed by researchers in these applied fields are instances of the general graphical model formalism. We review some of the basic ideas underlying graphical models, including the algorithmic ideas that allow graphical models to be deployed in large

TnT - A Statistical Part-Of-Speech Tagger

by Thorsten Brants , 2000
"... Trigrams'n'Tags (TnT) is an efficient statistical part-of-speech tagger. Contrary to claims found elsewhere in the literature, we argue that a tagger based on Markov models performs at least as well as other current approaches, including the Maximum Entropy framework. A recent comparison h ..."
Abstract - Cited by 540 (5 self) - Add to MetaCart
has even shown that TnT performs significantly better for the tested corpora. We describe the basic model of TnT, the techniques used for smoothing and for handling unknown words. Furthermore, we present evaluations on two corpora.

The Basic Model

by Main Model
"... description ..."
Abstract - Cited by 24 (0 self) - Add to MetaCart
description

Pictorial Structures for Object Recognition

by Pedro F. Felzenszwalb, Daniel P. Huttenlocher - IJCV , 2003
"... In this paper we present a statistical framework for modeling the appearance of objects. Our work is motivated by the pictorial structure models introduced by Fischler and Elschlager. The basic idea is to model an object by a collection of parts arranged in a deformable configuration. The appearance ..."
Abstract - Cited by 816 (15 self) - Add to MetaCart
In this paper we present a statistical framework for modeling the appearance of objects. Our work is motivated by the pictorial structure models introduced by Fischler and Elschlager. The basic idea is to model an object by a collection of parts arranged in a deformable configuration

and basic modeling

by E. Witrant
"... ..."
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Abstract not found

Basic Model Algorithm for the Basic Model

by Emmanuel Prados , 2006
"... A more Complex Model: the tree model Algorithm for the tree model ..."
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A more Complex Model: the tree model Algorithm for the tree model

Gaussian processes for machine learning

by Carl Edward Rasmussen , 2003
"... We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over functions. We present the simple equations for incorporating training data and examine how to learn the hyperparameters us ..."
Abstract - Cited by 720 (2 self) - Add to MetaCart
We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over functions. We present the simple equations for incorporating training data and examine how to learn the hyperparameters
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